The 2009 Report on Manufacturing Metal Fluid Power Valves and Hose Fittings: World Market Segmentation by City
ICON Group International, May 2009, Pages: 335
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings. 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings. 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 metal fluid power valves and hose fittings. 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 metal fluid power valves and hose fittings.
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 metal fluid power valves and hose fittings” 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing metal fluid power valves and hose fittings, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing metal fluid power valves and hose fittings is 332912. It is for this definition of manufacturing metal fluid power valves and hose fittings that the aggregate latent demand estimates are derived. “Manufacturing metal fluid power valves and hose fittings” is specifically defined as follows:
332912
This U.S. industry comprises establishments primarily engaged in manufacturing fluid power valves and hose fittings.
3329121
AEROSPACE TYPE HYDRAULIC FLUID POWER VALVES
33291211
Aerospace type hydraulic fluid power valves
3329121100
Aerospace type hydraulic fluid power valves
3329121110
Aerospace type hydraulic fluid power valves, 2_, 3_, and 4_way directional control, manually or mechanically operated
3329121120
Aerospace type hydraulic fluid power valves, 2_, 3_, and 4_way directional control, solenoid operated
3329121130
Aerospace type hydraulic fluid power valves, 2_, 3_, and 4_way directional control, motor operated
3329121140
Aerospace type hydraulic fluid power valves, 2_, 3_, and 4_way directional control, electrohydraulic servovalves
3329121150
Aerospace type hydraulic fluid power valves, other types (flow, pressure, etc.)
3329123
AEROSPACE TYPE PNEUMATIC FLUID POWER VALVES
33291231
Aerospace type pneumatic fluid power valves
3329123100
Aerospace type pneumatic fluid power valves
3329123110
Aerospace type pneumatic fluid power valves (all types)
3329125
NONAEROSPACE TYPE HYDRAULIC DIRECTIONAL CONTROL VALVES
33291251
Nonaerospace type hydraulic directional control valves
3329125100
Nonaerospace type hydraulic directional control valves
3329125110
Nonaerospace type hydraulic directional control valves (excluding cartridge, stack, logic, and electrohydraulic types), manual, 3_ and 4_way monoblock
3329125120
Nonaerospace type hydraulic directional control valves (excluding cartridge, stack, logic, and electrohydraulic types), manual, 3_ and 4_way sectional
3329125130
Nonaerospace type hydraulic directional control valves (excluding cartridge, stack, logic, and electrohydraulic types), other manual
3329125140
Nonaerospace type hydraulic directional control valves (excluding cartridge, stack, logic, and electrohydraulic types), solenoid, 2_way types
3329125150
Nonaerospace type hydraulic directional control valves (excluding cartridge, stack, logic, and electrohydraulic types), solenoid, 3_ and 4_way types
3329125160
Nonaerospace type hydraulic directional control valves (excluding cartridge, stack, logic, and electrohydraulic types), other directional control
3329127
NONAEROSPACE TYPE HYDRAULIC VALVES, EXCEPT DIRECTIONAL CONTROL
33291271
Nonaerospace type hydraulic valves, except directional control
3329127100
Nonaerospace type hydraulic valves, except directional control
3329127110
Nonaerospace type hydraulic (except directional control), pressure control, relief valves
3329127120
Nonaerospace type hydraulic (except directional control), pressure control, other (including load sensing and manual types)
3329127130
Nonaerospace type hydraulic (except directional control), flow control, manual types
3329127140
Nonaerospace type hydraulic (except directional control), flow control, other types (except manual)
3329127150
Nonaerospace type hydraulic (except directional control), cartidge valves (all types)
3329127160
Nonaerospace type hydraulic (except directional control), electrohydraulic valves, pilot operated, torque motor servovalves
3329127170
Nonaerospace type hydraulic (except directional control), electrohydraulic valves, other types (including proportional)
3329127180
Nonaerospace type hydraulic valves, other types, including stack and logic
3329129
NONAEROSPACE TYPE PNEUMATIC DIRECTIONAL CONTROL VALVES
33291291
Nonaerospace type pneumatic directional control valves
3329129100
Nonaerospace type pneumatic directional control valves
3329129110
Nonaerospace type pneumatic directional control valves, manual
3329129120
Nonaerospace type pneumatic directional control valves, solenoid, under 1/8 inch port diameter
3329129130
Nonaerospace type pneumatic directional control valves, solenoid, 1/8 inch up to and including 1/4 inch port diameter, 3_way types
3329129140
Nonaerospace type pneumatic directional control valves, solenoid, 1/8 inch up to and including 1/4 inch port diameter, 4_way types
3329129150
Nonaerospace type pneumatic directional control valves, solenoid, over 1/ 4 inch port diameter, 3_way types
3329129160
Nonaerospace type pneumatic directional control valves, solenoid, over 1/ 4 inch port diameter, 4_way types
3329129170
Nonaerospace type pneumatic directional control valves, all other solenoid types
3329129180
Nonaerospace type pneumatic directional control valves, mechanical, remote pilot and all other types
332912A
Aerospace-type hydraulic fluid power valves
332912B
Aerospace-type pneumatic fluid power valves
332912B1
Nonaerospace type pneumatic valves, except directional control
332912B100
Nonaerospace type pneumatic valves, except directional control
332912B110
Nonaerospace type pneumatic valves (except directional control), filter_ regulator combination units, integral types
332912B120
Nonaerospace type pneumatic valves (except directional control), other filter_regulators and filter_regulator_lubricator combination units
332912B130
Nonaerospace type pneumatic valves (except directional control), regulators sold separately
332912B140
Nonaerospace type pneumatic valves (except directional control), flow control valves
332912B150
Nonaerospace type pneumatic valves (except directional control), all other types (check, shuttle, exhaust, needle, etc.)
332912C
Nonaerospace-type hydraulic directional control valves
332912C11
Manual types
332912C12
All other, including solenoid types
332912D
Nonaerospace-type hydraulic valves, except directional control
332912D1
Parts for fluid power valves
332912D100
Parts for fluid power valves
332912D11
Cartridge valves
332912D110
Parts for nonaerospace type hydraulic and pneumatic valves, sold separately
332912D12
All other, including electrohydraulic
332912D120
Parts for aerospace type hydraulic and pneumatic valves, sold separately
332912E
Nonaerospace-type pneumatic directional control valves
332912E11
Solenoid types
332912E12
All other, including mechanical and remote pilot
332912F
Nonaerospace-type pneumatic valves, except directional control
332912F1
Aerospace type hydraulic and pneumatic fluid power hose or tube end fittings and assemblies
332912F100
Aerospace type hydraulic and pneumatic fluid power hose or tube end fittings and assemblies
332912F110
Aerospace type hydraulic and pneumatic assemblies of hose and/or tubing
332912F120
Aerospace type hydraulic and pneumatic quick connect, rotating, swivel, and extension fittings
332912F130
Aerospace type hydraulic and pneumatic manifolds for hydraulic systems
332912F140
All other aerospace type hydraulic and pneumatic hose and tube fittings
332912G
Parts for fluid power valves
332912H
Aerospace-type hydraulic and pneumatic hose or tube end fittings and assemblies
332912H1
Nonaerospace type flared (metal) fittings, couplings, and assemblies of tubing used in fluid power transfer systems
332912H100
Nonaerospace type flared (metal) fittings, couplings, and assemblies of tubing used in fluid power transfer systems
332912H110
Nonaerospace type flared (metal) fittings, for fluid power transfer systems, brass and bronze (SAE 45 degrees and 37 degrees)
332912H120
Nonaerospace type flared (metal) fittings, for fluid power transfer systems, carbon steel (JIC 37 degrees)
332912H130
Nonaerospace type flared (metal) fittings, for fluid power transfer systems, other (including alloy steel)
332912H140
Nonaerospace type assemblies of tubing, used in fluid power transfer systems
332912H150
Nonaerospace type assemblies of tubing and hose, used in fluid power transfer systems
332912J
Nonaerospace-type flared (metal) fittings, couplings for, and assemblies of tubi
332912J1
Nonaerospace type flareless fittings and couplings, including nonmetal fittings, used in fluid power transfer systems
332912J100
Nonaerospace type flareless fittings and couplings, including nonmetal fittings, used in fluid power transfer systems
332912J110
Nonaerospace type flareless fittings and couplings, used in fluid power transfer systems, brass and bronze
332912J120
Nonaerospace type flareless fittings and couplings, used in fluid power transfer systems, carbon steel
332912J130
Nonaerospace type flareless fittings and couplings, used in fluid power transfer systems, stainless steel
332912J140
Nonaerospace type flareless fittings and couplings, used in fluid power transfer systems, other metal typs (including alloy steel)
332912J150
Nonaerospace type flareless fittings and couplings, used in fluid power transfer systems, nonmetal (including plastic)
332912K
Nonaerospace-type flareless fittings and couplings (incl. nonmetal fittings) use
332912L
NONAEROSPACE TYPE HYDRAULIC AND PNEUMATIC FITTINGS AND COUPLINGS FOR HOSE
332912L1
Nonaerospace type hydraulic and pneumatic fittings and couplings for hose
332912L100
Nonaerospace type hydraulic and pneumatic fittings and couplings for hose
332912L110
Nonaerospace type hydraulic and pneumatic permanent hose end fittings (crimped and swaged)
332912L120
Nonaerospace type hydraulic and pneumatic reusable end fittings
332912L130
Nonaerospace type hydraulic quick connect and disconnect couplings
332912L140
Nonaerospace type pneumatic quick connect and disconnect couplings
332912L150
Nonaerospace type hydraulic and pneumatic manifolds and manifold assemblies for fluid power systems
332912L160
Nonaerospace type hydraulic and pneumatic fittings, all other (including steel adaptor, swivel, rotating, and extension fittings)
332912M
Miscellaneous receipts
332912N
Nonaerospace-type hydraulic and pneumatic assemblies of hose
332912N1
Nonaerospace type hydraulic and pneumatic assemblies of hose
332912N100
Nonaerospace type hydraulic and pneumatic assemblies of hose
332912N110
Nonaerospace type hydraulic and pneumatic assemblies of hose with permanent end fittings
332912N120
Nonaerospace type hydraulic and pneumatic assemblies of hose with reusable end fittings
332912O
Nonaerospace type hydraulic and pneumatic fittings and couplings for hose
332912P
Primary products
332912S
Secondary products
332912SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 332912 includes the following:
Control valves, fluid power, manufacturing
Electrohydraulic servo valves, fluid power, manufacturing
Fluid power aircraft subassemblies manufacturing
Fluid power hose assemblies manufacturing
Fluid power valves and hose fittings manufacturing
Hose assemblies for fluid power systems manufacturing
Hose couplings and fittings, fluid power, manufacturing
Hydraulic aircraft subassemblies manufacturing
Hydraulic hose fittings, fluid power, manufacturing
Hydraulic valves, fluid power, manufacturing
Pneumatic aircraft subassemblies manufacturing
Pneumatic hose fittings, fluid power, manufacturing
Pneumatic valves, fluid power, manufacturing
Pressure control valves, fluid power, manufacturing
Solenoid valves, fluid power, manufacturing
Tube and hose fittings, fluid power, manufacturing
Valves, hydraulic and pneumatic, fluid power, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings 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 metal fluid power valves and hose fittings). 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 21
1.2.3.3 Step 3. Filling in Missing Values 21
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 21
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 22
1.2.3.6 Step 6. Aggregation and Benchmarking 22
2 USING THE DATA 23
3 CITY SEGMENTS RANKED BY MARKET SIZE 24
3.1 Top 15 Markets 24
3.2 Markets 16 to 30 25
3.3 Remaining Cities by Market Rank 26
4 CITY SEGMENTS IN ALPHABETICAL ORDER 129
4.1 A: from Aalborg to Az Zawiyah 129
4.2 B: from Bacolod to Bydgoszcz 136
4.3 C: from Caaguazu to Cyangugu 144
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 174
4.12 L: from La Ceiba to Lyon 182
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 211
4.19 R: from Rabat to Rustavi 212
4.20 S: from S. Luis Potosi to Szombathely 215
4.21 T: from Tabligbo to Tyre 227
4.22 U: from Uberaba to Utulei 234
4.23 V: from Vacoas-Phoenix to Vukovar 236
4.24 W: from Wadi Medani to Wuhan 239
4.25 X: from Xalapa to Xian 240
4.26 Y: from Yamagata to Yungkang 241
4.27 Z: from Zadar to Zvishavane 242
5 CITY SEGMENTS RANKED BY COUNTRY 243
5.1 Afghanistan 243
5.2 Albania 243
5.3 Algeria 244
5.4 American Samoa 244
5.5 Andorra 244
5.6 Angola 245
5.7 Antigua and Barbuda 245
5.8 Argentina 246
5.9 Armenia 247
5.10 Aruba 247
5.11 Australia 248
5.12 Austria 248
5.13 Azerbaijan 249
5.14 Bahrain 249
5.15 Bangladesh 249
5.16 Barbados 250
5.17 Belarus 250
5.18 Belgium 250
5.19 Belize 251
5.20 Benin 251
5.21 Bermuda 251
5.22 Bhutan 252
5.23 Bolivia 252
5.24 Bosnia and Herzegovina 252
5.25 Botswana 253
5.26 Brazil 254
5.27 Brunei 259
5.28 Bulgaria 259
5.29 Burkina Faso 260
5.30 Burma 260
5.31 Burundi 260
5.32 Cambodia 261
5.33 Cameroon 261
5.34 Canada 261
5.35 Cape Verde 262
5.36 Central African Republic 262
5.37 Chad 262
5.38 Chile 263
5.39 China 263
5.40 Christmas Island 264
5.41 Colombia 264
5.42 Comoros 264
5.43 Congo (formerly Zaire) 265
5.44 Cook Islands 265
5.45 Costa Rica 265
5.46 Cote dIvoire 266
5.47 Croatia 266
5.48 Cuba 267
5.49 Cyprus 267
5.50 Czech Republic 267
5.51 Denmark 268
5.52 Djibouti 268
5.53 Dominica 268
5.54 Dominican Republic 269
5.55 Ecuador 269
5.56 Egypt 270
5.57 El Salvador 270
5.58 Equatorial Guinea 270
5.59 Estonia 271
5.60 Ethiopia 271
5.61 Fiji 271
5.62 Finland 272
5.63 France 272
5.64 French Guiana 273
5.65 French Polynesia 273
5.66 Gabon 273
5.67 Georgia 274
5.68 Germany 274
5.69 Ghana 274
5.70 Greece 275
5.71 Greenland 275
5.72 Grenada 275
5.73 Guadeloupe 276
5.74 Guam 276
5.75 Guatemala 276
5.76 Guinea 277
5.77 Guinea-Bissau 277
5.78 Guyana 277
5.79 Haiti 278
5.80 Honduras 278
5.81 Hong Kong 278
5.82 Hungary 279
5.83 Iceland 279
5.84 India 280
5.85 Indonesia 281
5.86 Iran 282
5.87 Iraq 282
5.88 Ireland 283
5.89 Israel 283
5.90 Italy 284
5.91 Jamaica 284
5.92 Japan 285
5.93 Jordan 287
5.94 Kazakhstan 288
5.95 Kenya 288
5.96 Kiribati 289
5.97 Kuwait 289
5.98 Kyrgyzstan 289
5.99 Laos 289
5.100 Latvia 290
5.101 Lebanon 290
5.102 Lesotho 290
5.103 Liberia 291
5.104 Libya 291
5.105 Liechtenstein 291
5.106 Lithuania 292
5.107 Luxembourg 292
5.108 Macau 292
5.109 Madagascar 293
5.110 Malawi 293
5.111 Malaysia 294
5.112 Maldives 294
5.113 Mali 295
5.114 Malta 295
5.115 Marshall Islands 295
5.116 Martinique 296
5.117 Mauritania 296
5.118 Mauritius 296
5.119 Mexico 297
5.120 Micronesia Federation 298
5.121 Moldova 298
5.122 Monaco 298
5.123 Mongolia 298
5.124 Morocco 299
5.125 Mozambique 299
5.126 Namibia 299
5.127 Nauru 300
5.128 Nepal 300
5.129 New Caledonia 300
5.130 New Zealand 301
5.131 Nicaragua 301
5.132 Niger 302
5.133 Nigeria 302
5.134 Niue 302
5.135 Norfolk Island 303
5.136 North Korea 303
5.137 Norway 303
5.138 Oman 304
5.139 Pakistan 304
5.140 Palau 304
5.141 Palestine 304
5.142 Panama 305
5.143 Papua New Guinea 305
5.144 Paraguay 305
5.145 Peru 306
5.146 Philippines 306
5.147 Poland 307
5.148 Portugal 307
5.149 Puerto Rico 308
5.150 Qatar 308
5.151 Republic of Congo 308
5.152 Reunion 309
5.153 Romania 309
5.154 Russia 310
5.155 Rwanda 310
5.156 San Marino 310
5.157 Sao Tome E Principe 311
5.158 Saudi Arabia 311
5.159 Senegal 311
5.160 Seychelles 312
5.161 Sierra Leone 312
5.162 Singapore 312
5.163 Slovakia 312
5.164 Slovenia 313
5.165 Solomon Islands 313
5.166 Somalia 313
5.167 South Africa 314
5.168 South Korea 314
5.169 Spain 315
5.170 Sri Lanka 315
5.171 St. Kitts and Nevis 316
5.172 St. Lucia 316
5.173 St. Vincent and the Grenadines 316
5.174 Sudan 316
5.175 Suriname 317
5.176 Swaziland 317
5.177 Sweden 317
5.178 Switzerland 318
5.179 Syrian Arab Republic 318
5.180 Taiwan 319
5.181 Tajikistan 320
5.182 Tanzania 320
5.183 Thailand 320
5.184 The Bahamas 321
5.185 The British Virgin Islands 321
5.186 The Cayman Islands 321
5.187 The Falkland Islands 321
5.188 The Gambia 322
5.189 The Netherlands 322
5.190 The Netherlands Antilles 323
5.191 The Northern Mariana Island 323
5.192 The U.S. Virgin Islands 323
5.193 The United Arab Emirates 324
5.194 The United Kingdom 324
5.195 The United States 325
5.196 Togo 326
5.197 Tokelau 326
5.198 Tonga 326
5.199 Trinidad and Tobago 327
5.200 Tunisia 327
5.201 Turkey 328
5.202 Turkmenistan 328
5.203 Tuvalu 328
5.204 Uganda 329
5.205 Ukraine 329
5.206 Uruguay 330
5.207 Uzbekistan 330
5.208 Vanuatu 330
5.209 Venezuela 331
5.210 Vietnam 331
5.211 Wallis and Futuna 332
5.212 Western Sahara 332
5.213 Western Samoa 332
5.214 Yemen 332
5.215 Zambia 333
5.216 Zimbabwe 333
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 334
6.1 Disclaimers & Safe Harbor 334
6.2 ICON Group International, Inc. User Agreement Provisions 335
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