The 2009 Report on Manufacturing Electric Lighting Fixtures and Non-Electric Lighting Equipment Excluding Residential, Commercial, Industrial, Institutional, and Vehicular Electric Lighting Fixtures: World Market Segmentation by City
ICON Group International, May 2009, Pages: 358
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures. 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures. 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures. 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures.
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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures” 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures is 335129. It is for this definition of manufacturing electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures that the aggregate latent demand estimates are derived. “Manufacturing electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures” is specifically defined as follows:
335129
This U.S. industry comprises establishments primarily engaged in manufacturing electric lighting fixtures (except residential, commercial, industrial, institutional, and vehicular electric lighting fixtures) and nonelectric lighting equipment.
3351291
Outdoor lighting equipment (including parts and accessories)
33512910
Outdoor lighting equipment (including parts and accessories)
3351291000
Outdoor lighting equipment (including parts and accessories)
3351291002
Incandescent (filament and quartz iodine) street and highway lighting (including bridge and tunnel lighting)
3351291004
Open high intensity discharge (including low pressure sodium and integrally mounted and remote ballasts) street and highway lighting (including bridge and tunnel lighting)
3351291006
Enclosed high intensity discharge (including low pressure sodium and integrally mounted and remote ballasts) street and highway lighting (including bridge and tunnel lighting)
3351291008
Fluorescent street and highway lighting (including bridge and tunnel lighting)
3351291011
Special purpose luminaries for highmast, sign lighting, and expressway fixtures (excluding value of poles)
3351291012
General purpose incandescent filament floodlighting
3351291014
General purpose incandescent quartz iodine floodlighting
3351291016
General purpose high intensity discharge floodlighting (including low presure sodium and integrally mounted and remote ballasts)
3351291018
General purpose fluorescent floodlighting
3351291021
High intensity discharge sportslighting
3351291022
High intensity discharge site lighting (under 20 foot mounting)
3351291024
High intensity discharge bollards
3351291026
High intensity discharge post~top lighting
3351291028
High intensity discharge large area lighting (20 to 60 foot mounting)
3351291031
High intensity discharge, incandescent, and quartz wall packs
3351291032
Outdoor PAR lampholders
3351291034
Spotlights (including indoor and stage)
3351291036
All other outdoor lighting equipment (such as underwater fountain and pool lighting)
3351291038
Runway approach lighting (including fixtures, regulators, insulating transformers, isolating lamp transformers, beacons, wind tees, and cones)
3351291041
Runway (except runway approach), taxiway, and ramp light (including fixtures, regulators, and isolating lamp transformers)
3351291042
Components and renewal parts for outdoor lighting equipment sold separately
3351291044
Steel and cast iron poles for street and highway lighting
3351291046
Aluminum poles for street and highway lighting
3351291048
Concrete poles for street and highway lighting
3351291051
All other street and highway lighting (including fiberglass and wood)
3351291052
Steel and cast iron area lighting poles for sports and other off~street use (60 foot and over)
3351291054
Steel and cast iron area lighting poles for sports and other off~street use (under 60 foot)
3351291056
Aluminum poles for area lighting for sports and other off~street use
3351291058
All other area lighting for sports and other off~street use (including concrete and wood)
33512911
Outdoor lighting equipment (including parts and accessories)
3351291100
Outdoor lighting equipment (including parts and accessories)
3351293
Electric and nonelectric lighting equipment, nec incl. parts and accessories
33512931
Electric and nonelectric lighting equipment, nec, including hand portable, parts and accessories
3351293109
Rechargeable battery~operated incandescent hand portable lighting equipment, excluding parts and accessories
3351293112
Incandescent hand portable flashlights and flashlight lanterns, other than rechargeable battery~operated
3351293114
Other incandescent hand portable lighting equipment, other than recargeable battery~operated, such as miners’ lights, emergency warning lights, generator flashlights, etc.
3351293116
Other incandescent electric lighting equipment (including marine markers or beacons)
3351293118
Other fluorescent lighting equipment, complete units, including processing and technical equipment
3351293122
Other electric lighting equipment such as mercury vapor (other than street and highway lighting equipment) sodium vapor (excluding signs) ultraviolet and infrared health lamp fixtures
3351293124
Parts and accessories for other electric lighting fixtures, nec
3351293126
Nonelectric lighting fixtures and equipment, complete units (including lamps and lanterns ~ kerosene, gasoline, propane, butane, etc., and carbide lamps of all types)
3351293131
Parts and accessories for nonelectric lighting equipment, including reflectors and fittings, incandescent mantles, etc.)
3351294
ALL OTHER MISCELLANEOUS ELECTRIC AND NONELECTRIC LIGHTING EQUIPMENT, INCLUDING HAND PORTABLE, PARTS AND ACCESSORIES
33512941
All other miscellaneous electric and nonelectric lighting equipment, including hand portable, parts and accessories
3351294109
Rechargeable battery_operated incandescent hand portable lighting equipment, excluding parts and accessories
3351294112
Incandescent hand portable flashlights and flashlight lanterns, except rechargeable battery_operated
3351294114
Other incandescent hand portable lighting equipment (including miners’ lights, emergency warning lights, generator flashlights, etc.), except recargeable battery_operated
3351294116
Other incandescent electric lighting equipment (including marine markers and beacons)
3351294118
Other fluorescent lighting equipment, complete units, including processing and technical equipment
3351294122
Other electric lighting equipment (including mercury and sodium vapor, and ultraviolet and infrared health lamp fixtures), except street and highway lighting equipment and signs
3351294123
Electric fireplace logs
3351294124
Parts and accessories for all other miscellaneous electric lighting fixtures
3351294125
Electric trouble lighting equipment
3351294126
Nonelectric lighting fixtures and equipment, complete units (including lamps and lanterns using kerosene, gasoline, propane, butane, etc., and carbide lamps of all types)
3351294127
Electric insect killers
3351294129
Electric insect repellent lamps
3351294131
Parts and accessories for nonelectric lighting equipment (including reflectors and fittings, incandescent mantles, etc.)
335129M
Miscellaneous receipts
335129P
Primary products
335129S
Secondary products
335129SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 335129 includes the following:
Arc lighting fixtures (except electrotherapeutic), electric, manufacturing
Area and sports luminaries (e.g., stadium lighting fixtures), electric, manufactu
Christmas tree lighting sets, electric, manufacturing
Decorative area lighting fixtures (except residential) manufacturing
Fireplace logs, electric, manufacturing
Flashlights manufacturing
Floodlights (i.e., lighting fixtures) manufacturing
Flytraps, electrical, manufacturing
Fountain lighting fixtures manufacturing
Gas lighting fixtures manufacturing
Infrared lamp fixtures manufacturing
Insect lamps, electric, manufacturing
Lamps, insect, electric fixture, manufacturing
Lanterns (e.g., carbide, electric, gas, gasoline, kerosene) manufacturing
Lighting fixtures, airport (e.g., approach, ramp, runway, taxi), manufacturing
Lighting fixtures, nonelectric (e.g., propane, kerosene, carbide), manufacturing
Miners lamps manufacturing
Ornaments, Christmas tree, electric, manufacturing
Prewired poles, brackets, and accessories for electric lighting, manufacturing
Reflectors for lighting equipment, metal, manufacturing
Searchlights, electric and nonelectric, manufacturing
Spotlights (except vehicular) manufacturing
Stage lighting equipment manufacturing
Street lighting fixtures (except traffic signals) manufacturing
Swimming pool lighting fixtures manufacturing
Trouble lights manufacturing
Ultraviolet lamp fixtures manufacturing
Underwater lighting fixtures manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures 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 electric lighting fixtures and non-electric lighting equipment excluding residential, commercial, industrial, institutional, and vehicular electric lighting fixtures). 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 13
1.2.3.1 Step 1. Product Definition and Data Collection 14
1.2.3.2 Step 2. Filtering and Smoothing 19
1.2.3.3 Step 3. Filling in Missing Values 19
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 20
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 20
1.2.3.6 Step 6. Aggregation and Benchmarking 20
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 152
4.5 E: from East London to Esteli 156
4.6 F: from Fagatogo to Funchal 158
4.7 G: from Gabes to Gyumri 161
4.8 H: from Hachinohe to Hyderabad 165
4.9 I: from Iasi to Izmir 169
4.10 J: from Jaboatao to Jyvaskyla 172
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 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 214
4.19 R: from Rabat to Rustavi 215
4.20 S: from S. Luis Potosi to Szombathely 218
4.21 T: from Tabligbo to Tyre 230
4.22 U: from Uberaba to Utulei 238
4.23 V: from Vacoas-Phoenix to Vukovar 240
4.24 W: from Wadi Medani to Wuhan 243
4.25 X: from Xalapa to Xian 244
4.26 Y: from Yamagata to Yungkang 245
4.27 Z: from Zadar to Zvishavane 246
5 CITY SEGMENTS RANKED BY COUNTRY 248
5.1 Afghanistan 248
5.2 Albania 248
5.3 Algeria 249
5.4 American Samoa 249
5.5 Andorra 250
5.6 Angola 250
5.7 Antigua and Barbuda 250
5.8 Argentina 251
5.9 Armenia 252
5.10 Aruba 252
5.11 Australia 253
5.12 Austria 253
5.13 Azerbaijan 254
5.14 Bahrain 254
5.15 Bangladesh 255
5.16 Barbados 255
5.17 Belarus 256
5.18 Belgium 256
5.19 Belize 257
5.20 Benin 257
5.21 Bermuda 257
5.22 Bhutan 258
5.23 Bolivia 258
5.24 Bosnia and Herzegovina 259
5.25 Botswana 259
5.26 Brazil 260
5.27 Brunei 265
5.28 Bulgaria 266
5.29 Burkina Faso 266
5.30 Burma 267
5.31 Burundi 267
5.32 Cambodia 267
5.33 Cameroon 268
5.34 Canada 268
5.35 Cape Verde 269
5.36 Central African Republic 269
5.37 Chad 270
5.38 Chile 270
5.39 China 271
5.40 Christmas Island 271
5.41 Colombia 272
5.42 Comoros 272
5.43 Congo (formerly Zaire) 273
5.44 Cook Islands 273
5.45 Costa Rica 274
5.46 Cote dIvoire 274
5.47 Croatia 275
5.48 Cuba 275
5.49 Cyprus 276
5.50 Czech Republic 276
5.51 Denmark 277
5.52 Djibouti 277
5.53 Dominica 278
5.54 Dominican Republic 278
5.55 Ecuador 279
5.56 Egypt 279
5.57 El Salvador 280
5.58 Equatorial Guinea 280
5.59 Estonia 280
5.60 Ethiopia 281
5.61 Fiji 281
5.62 Finland 282
5.63 France 282
5.64 French Guiana 283
5.65 French Polynesia 283
5.66 Gabon 283
5.67 Georgia 284
5.68 Germany 284
5.69 Ghana 285
5.70 Greece 285
5.71 Greenland 286
5.72 Grenada 286
5.73 Guadeloupe 287
5.74 Guam 287
5.75 Guatemala 288
5.76 Guinea 288
5.77 Guinea-Bissau 288
5.78 Guyana 289
5.79 Haiti 289
5.80 Honduras 290
5.81 Hong Kong 290
5.82 Hungary 291
5.83 Iceland 291
5.84 India 292
5.85 Indonesia 293
5.86 Iran 294
5.87 Iraq 294
5.88 Ireland 295
5.89 Israel 295
5.90 Italy 296
5.91 Jamaica 296
5.92 Japan 297
5.93 Jordan 300
5.94 Kazakhstan 300
5.95 Kenya 301
5.96 Kiribati 301
5.97 Kuwait 301
5.98 Kyrgyzstan 302
5.99 Laos 302
5.100 Latvia 302
5.101 Lebanon 303
5.102 Lesotho 303
5.103 Liberia 303
5.104 Libya 304
5.105 Liechtenstein 304
5.106 Lithuania 305
5.107 Luxembourg 305
5.108 Macau 305
5.109 Madagascar 306
5.110 Malawi 306
5.111 Malaysia 307
5.112 Maldives 307
5.113 Mali 308
5.114 Malta 308
5.115 Marshall Islands 308
5.116 Martinique 309
5.117 Mauritania 309
5.118 Mauritius 310
5.119 Mexico 311
5.120 Micronesia Federation 312
5.121 Moldova 312
5.122 Monaco 312
5.123 Mongolia 313
5.124 Morocco 313
5.125 Mozambique 314
5.126 Namibia 314
5.127 Nauru 314
5.128 Nepal 315
5.129 New Caledonia 315
5.130 New Zealand 316
5.131 Nicaragua 316
5.132 Niger 317
5.133 Nigeria 317
5.134 Niue 318
5.135 Norfolk Island 318
5.136 North Korea 318
5.137 Norway 319
5.138 Oman 319
5.139 Pakistan 320
5.140 Palau 320
5.141 Palestine 320
5.142 Panama 321
5.143 Papua New Guinea 321
5.144 Paraguay 322
5.145 Peru 322
5.146 Philippines 323
5.147 Poland 324
5.148 Portugal 324
5.149 Puerto Rico 325
5.150 Qatar 325
5.151 Republic of Congo 326
5.152 Reunion 326
5.153 Romania 327
5.154 Russia 327
5.155 Rwanda 328
5.156 San Marino 328
5.157 Sao Tome E Principe 328
5.158 Saudi Arabia 329
5.159 Senegal 329
5.160 Seychelles 330
5.161 Sierra Leone 330
5.162 Singapore 330
5.163 Slovakia 331
5.164 Slovenia 331
5.165 Solomon Islands 331
5.166 Somalia 332
5.167 South Africa 332
5.168 South Korea 333
5.169 Spain 334
5.170 Sri Lanka 334
5.171 St. Kitts and Nevis 335
5.172 St. Lucia 335
5.173 St. Vincent and the Grenadines 335
5.174 Sudan 336
5.175 Suriname 336
5.176 Swaziland 337
5.177 Sweden 337
5.178 Switzerland 338
5.179 Syrian Arab Republic 338
5.180 Taiwan 339
5.181 Tajikistan 340
5.182 Tanzania 340
5.183 Thailand 341
5.184 The Bahamas 341
5.185 The British Virgin Islands 341
5.186 The Cayman Islands 342
5.187 The Falkland Islands 342
5.188 The Gambia 342
5.189 The Netherlands 343
5.190 The Netherlands Antilles 343
5.191 The Northern Mariana Island 343
5.192 The U.S. Virgin Islands 344
5.193 The United Arab Emirates 344
5.194 The United Kingdom 345
5.195 The United States 346
5.196 Togo 347
5.197 Tokelau 347
5.198 Tonga 348
5.199 Trinidad and Tobago 348
5.200 Tunisia 348
5.201 Turkey 349
5.202 Turkmenistan 349
5.203 Tuvalu 350
5.204 Uganda 350
5.205 Ukraine 351
5.206 Uruguay 351
5.207 Uzbekistan 352
5.208 Vanuatu 352
5.209 Venezuela 353
5.210 Vietnam 354
5.211 Wallis and Futuna 354
5.212 Western Sahara 354
5.213 Western Samoa 355
5.214 Yemen 355
5.215 Zambia 355
5.216 Zimbabwe 356
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 357
6.1 Disclaimers & Safe Harbor 357
6.2 ICON Group International, Inc. User Agreement Provisions 358
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