The 2009 Report on Commercial and Service Industry Machinery Manufacturing Excluding Automatic Vending Machines, Commercial Laundry, Dry Cleaning and Pressing Machines, Office Machinery, Optical Instruments and Lenses, and Photographic and Photocopyi
ICON Group International, May 2009, Pages: 367
Market Potential Estimation Methodology
Overview
This study covers the world outlook for commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment. 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment. 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment. 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment.
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 “commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment” 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 “commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment is 333319. It is for this definition of commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment that the aggregate latent demand estimates are derived. “Commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment” is specifically defined as follows:
333319
This U.S. industry comprises establishments primarily engaged in manufacturing commercial and service industry equipment (except automatic vending machines, commercial laundry, drycleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment).
3333191
Commercial cooking & food warming equipment, incl. parts & attachments
33331911
Commercial cooking equipment, including ranges, deep_fat fryers, griddles, toasters, coffee urns, pressure cookers (steam), etc., except electric
3333191111
Commercial ranges, ovens, and broilers (except electric)
3333191116
Commercial deep_fat fryers (except electric)
3333191121
Other commercial cooking equipment (griddles, toasters, coffee urns, pressure cookers (steam), etc.), except electric
3333191131
Commercial food_warming equipment, including steam tables (except electric)
33331912
Commercial electric cooking equipment, including ranges, deep_fat fryers, griddles, toasters, coffe makers, coffee urns, etc.
3333191241
Commercial electric ranges, ovens, and broilers
3333191251
Commercial microwave stoves and ovens
3333191261
Commercial electric deep_fat fryers
3333191271
Other commercial electric cooking equipment, including griddles, toasters, coffee makers, coffee urns, etc.
3333191281
Commercial electric food_warming equipment, including hot_food server units and steam tables
33331913
Parts and accessories for commercial cooking and food_warming equipment
3333191391
Parts and accessories for commercial cooking and food_warming equipment
3333193
Commercial & industrial vacuum cleaners, incl. parts & attachments
33331931
Commercial and industrial central and portable vacuum cleaner systems, including parts and attachments
3333193101
Commercial and industrial portable vacuum cleaners
3333193111
Parts and attachments for commercial and industrial portable vacuum claeners
33331932
Commercial and industrial central vacuum cleaner systems, including parts and attachments
3333193221
Commercial and industrial central vacuum cleaner systems, including parts and attachments
3333195
Automotive maintenance equipment, except handtools
33331951
Automotive maintenance equipment, except handtools
3333195101
Automotive frame and body alignment equipment, except handtools
3333195106
Automotive wheel alignment equipment, except handtools
3333195111
Automotive wheel balancing equipment, except handtools
3333195116
Automotive tire and wheel mounting equipment, except handtools
3333195121
Automotive brake service equipment, except handtools
3333195126
All other automotive maintenance equipment, except handtools
33331952
Parts and attachments for automotive maintenance equipment, except handtools (sold separately)
3333195231
Parts and attachments for automotive maintenance equipment, except handtools (sold separately)
3333197
Electronic teaching machines, teaching aids, trainers, & simulators, incl. kits
33331970
Electronic teaching machines, teaching aids, trainers, and simulators, including kits
3333197000
Electronic teaching machines, teaching aids, trainers, and simulators, including kits
3333197001
Electronic trainers and simulators
3333197004
All other electronic teaching machines and teaching aids
33331971
Electronic teaching machines, teaching aids, trainers, and simulators, including kits
3333197100
Electronic teaching machines, teaching aids, trainers, and simulators, including kits
3333197101
Electronic trainers and simulators
3333197104
Other electronic teaching machines and teaching aids
3333199
Miscellaneous machinery products, except electrical
33331991
Other service industry equipment
3333199101
Instantaneous service industry water heaters, including parts
3333199106
All others service industry water heaters (including parts) with more than 120 gallons (454.2 liters capacity)
3333199111
Industrial water softeners
3333199116
Farm, household, and commercial water softeners
3333199146
Conveyor~type commercial dishwashing machines
3333199151
All other commercial dishwashing machines
3333199161
Sewage treatment equipment, distilling or rectifying
3333199166
Sewage treatment equipment, filtering or purifying
3333199171
Sewage treatment equipment, centrifuges
3333199174
Other sewage treatment equipment
3333199178
Commercial car, truck, and bus washing machinery and equipment
3333199182
Service industry trash and garbage compactors
3333199186
Sewer pipe and drain cleaning equipment
3333199189
High~pressure (more than 1,000 p.s.i.) cleaning and blasting machinery and equipment (except foundry)
3333199194
Electric hand~drying apparatus
3333199196
Other service industry equipment
33331992
All other parts and attachments for service industry equipment
3333199221
Parts for water softeners (excluding tanks)
3333199236
Parts and attachments for commercial and industrial floor and carpet cleaning equipment
3333199256
Parts for commercial dishwashing machines
3333199299
Parts for other service industry machines
33331993
Commercial and industrial floor and carpet cleaning machines, including waxing and polishing machines, except vacuum cleaners.
3333199301
Carnival and amusement park equipment (ferris wheels, merry~go~rounds, etc.), excluding electric equipment, and coin~operated amusement machines
3333199326
Commercial and industrial floor sanding and scrubbing machines
3333199331
Commercial and industrial carpet cleaning equipment, including sweepers (except vacuum cleaners)
3333199341
Other commercial and industrial floor and carpet cleaning machines, including waxing and polishing machines, except vacuum cleaners
333319A
MISCELLANEOUS MACHINERY PRODUCTS (INCLUDING FLEXIBLE METAL HOSE AND TUBING, METAL BELLOWS, ETC.), EXCEPT ELECTRICAL
333319A1
Other service industry equipment
333319A101
Instantaneous service industry water heaters, including parts
333319A106
All others service industry water heaters (including parts), more than 120 gallons (454.2 liters) capacity
333319A111
Industrial water softeners
333319A116
Farm, household, and commercial water softeners
333319A146
Conveyor_type commercial dishwashing machines
333319A151
All other commercial dishwashing machines
333319A161
Sewage treatment equipment, distilling or rectifying
333319A166
Sewage treatment equipment, filtering or purifying
333319A171
Sewage treatment equipment, centrifuges
333319A174
Other sewage treatment equipment
333319A178
Commercial car, truck, and bus washing machinery and equipment
333319A182
Service industry trash and garbage compactors
333319A186
Sewer pipe and drain cleaning equipment
333319A189
High_pressure (more than 1,000 p.s.i.) cleaning and blasting machinery and equipment (except foundry)
333319A191
Barber and beauty shop furniture and equipment, excluding barber and beauty chairs
333319A194
Electric hand_drying apparatus
333319A196
Other service industry equipment
333319A2
All other parts and attachments for service industry equipment
333319A221
Parts for water softeners (excluding tanks)
333319A236
Parts and attachments for commercial and industrial floor and carpet cleaning equipment
333319A256
Parts for commercial dishwashing machines
333319A299
Parts for other service industry machines
333319A3
Commercial and industrial floor and carpet cleaning machines, including waxing and polishing machines, except vacuum cleaners
333319A301
Carnival and amusement park equipment (ferris wheels, merry_go_rounds, etc.), excluding electric equipment, and coin_operated amusement machines
333319A326
Commercial and industrial floor sanding and scrubbing machines
333319A331
Commercial and industrial carpet cleaning equipment, including sweepers (except vacuum cleaners)
333319A341
Other commercial and industrial floor and carpet cleaning machines, including waxing and polishing machines, except vacuum cleaners
333319M
Miscellaneous receipts
333319P
Primary products
333319S
Secondary products
333319SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 333319 includes the following:
Alignment equipment, motor vehicle, manufacturing
Balancing equipment, motor vehicle, manufacturing
Brake service equipment (except mechanics hand tools), motor vehicle, manufactur
Carnival and amusement park rides manufacturing
Carnival and amusement park shooting gallery machinery manufacturing
Carousels (i.e., merry-go-rounds) manufacturing
Carpet and floor cleaning equipment, electric commercial-type, manufacturing
Carpet sweepers, mechanical, manufacturing
Carwashing machinery manufacturing
Central vacuuming systems, commercial-type, manufacturing
Coffee makers and urns, commercial-type, manufacturing
Cooking equipment (i.e., fryers, microwave ovens, ovens, ranges), commercial-type
Corn popping machines, commercial-type, manufacturing
Deep-fat fryers, commercial-type, manufacturing
Dishwashing machines, commercial-type, manufacturing
Ferris wheels manufacturing
Flight simulation machinery manufacturing
Floor sanding, washing, and polishing machines, commercial-type, manufacturing
Food warming equipment, commercial-type, manufacturing
Frame and body alignment equipment, motor vehicle, manufacturing
Garbage disposal units, commercial-type, manufacturing
Gas ranges, commercial-type, manufacturing
Hair dryers, beauty parlor-type, manufacturing
Hotplates, commercial-type, manufacturing
Microwave ovens, commercial-type, manufacturing
Mop wringers manufacturing
Ovens, commercial-type, manufacturing
Ozone machines for water purification manufacturing
Power washer cleaning equipment manufacturing
Ranges, commercial-type, manufacturing
Sanding machines, floor, manufacturing
Sewage treatment equipment manufacturing
Steam cookers, commercial-type, manufacturing
Steam tables manufacturing
Stoves, commercial-type, manufacturing
Swimming pool filter systems manufacturing
Teaching machines (e.g., flight simulators) manufacturing
Tire mounting machines, motor vehicle, manufacturing
Trash and garbage compactors, commercial-type, manufacturing
Vacuum cleaners, industrial and commercial-type, manufacturing
Water heaters (except boilers), commercial-type, manufacturing
Water purification equipment manufacturing
Water softening equipment manufacturing
Water treatment equipment manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment 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 commercial and service industry machinery manufacturing excluding automatic vending machines, commercial laundry, dry cleaning and pressing machines, office machinery, optical instruments and lenses, and photographic and photocopying equipment is assumed to approach zero. The ass
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 22
1.2.3.3 Step 3. Filling in Missing Values 22
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 22
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 23
1.2.3.6 Step 6. Aggregation and Benchmarking 23
2 USING THE DATA 24
3 CITY SEGMENTS RANKED BY MARKET SIZE 25
3.1 Top 15 Markets 25
3.2 Markets 16 to 30 25
3.3 Remaining Cities by Market Rank 27
4 CITY SEGMENTS IN ALPHABETICAL ORDER 130
4.1 A: from Aalborg to Az Zawiyah 130
4.2 B: from Bacolod to Bydgoszcz 137
4.3 C: from Caaguazu to Cyangugu 145
4.4 D: from Da Nang to Dzhizak 154
4.5 E: from East London to Esteli 158
4.6 F: from Fagatogo to Funchal 160
4.7 G: from Gabes to Gyumri 163
4.8 H: from Hachinohe to Hyderabad 167
4.9 I: from Iasi to Izmir 171
4.10 J: from Jaboatao to Jyvaskyla 174
4.11 K: from Kabul to Kzyl-Orda 177
4.12 L: from La Ceiba to Lyon 185
4.13 M: from Macae to Mzuzu 191
4.14 N: from Nacala to Nzerekore 201
4.15 O: from Oaklahoma City to Oyem 206
4.16 Ö: from Örebro to Örebro 208
4.17 P: from Pago Pago to Pyuthan 209
4.18 Q: from Qandahar to Quito 216
4.19 R: from Rabat to Rustavi 217
4.20 S: from S. Luis Potosi to Szombathely 220
4.21 T: from Tabligbo to Tyre 232
4.22 U: from Uberaba to Utulei 240
4.23 V: from Vacoas-Phoenix to Vukovar 242
4.24 W: from Wadi Medani to Wuhan 245
4.25 X: from Xalapa to Xian 246
4.26 Y: from Yamagata to Yungkang 247
4.27 Z: from Zadar to Zvishavane 248
5 CITY SEGMENTS RANKED BY COUNTRY 250
5.1 Afghanistan 250
5.2 Albania 250
5.3 Algeria 251
5.4 American Samoa 251
5.5 Andorra 252
5.6 Angola 252
5.7 Antigua and Barbuda 252
5.8 Argentina 253
5.9 Armenia 254
5.10 Aruba 254
5.11 Australia 255
5.12 Austria 255
5.13 Azerbaijan 256
5.14 Bahrain 256
5.15 Bangladesh 257
5.16 Barbados 257
5.17 Belarus 258
5.18 Belgium 258
5.19 Belize 259
5.20 Benin 259
5.21 Bermuda 259
5.22 Bhutan 260
5.23 Bolivia 260
5.24 Bosnia and Herzegovina 261
5.25 Botswana 261
5.26 Brazil 261
5.27 Brunei 267
5.28 Bulgaria 268
5.29 Burkina Faso 268
5.30 Burma 269
5.31 Burundi 269
5.32 Cambodia 270
5.33 Cameroon 270
5.34 Canada 271
5.35 Cape Verde 271
5.36 Central African Republic 271
5.37 Chad 272
5.38 Chile 273
5.39 China 274
5.40 Christmas Island 274
5.41 Colombia 275
5.42 Comoros 275
5.43 Congo (formerly Zaire) 276
5.44 Cook Islands 276
5.45 Costa Rica 277
5.46 Cote dIvoire 277
5.47 Croatia 278
5.48 Cuba 278
5.49 Cyprus 278
5.50 Czech Republic 279
5.51 Denmark 280
5.52 Djibouti 280
5.53 Dominica 281
5.54 Dominican Republic 281
5.55 Ecuador 282
5.56 Egypt 282
5.57 El Salvador 283
5.58 Equatorial Guinea 283
5.59 Estonia 284
5.60 Ethiopia 284
5.61 Fiji 285
5.62 Finland 285
5.63 France 286
5.64 French Guiana 286
5.65 French Polynesia 287
5.66 Gabon 287
5.67 Georgia 288
5.68 Germany 288
5.69 Ghana 289
5.70 Greece 289
5.71 Greenland 289
5.72 Grenada 290
5.73 Guadeloupe 291
5.74 Guam 291
5.75 Guatemala 292
5.76 Guinea 292
5.77 Guinea-Bissau 293
5.78 Guyana 293
5.79 Haiti 294
5.80 Honduras 294
5.81 Hong Kong 295
5.82 Hungary 295
5.83 Iceland 296
5.84 India 297
5.85 Indonesia 298
5.86 Iran 299
5.87 Iraq 300
5.88 Ireland 300
5.89 Israel 300
5.90 Italy 301
5.91 Jamaica 301
5.92 Japan 303
5.93 Jordan 306
5.94 Kazakhstan 306
5.95 Kenya 307
5.96 Kiribati 307
5.97 Kuwait 308
5.98 Kyrgyzstan 308
5.99 Laos 309
5.100 Latvia 309
5.101 Lebanon 310
5.102 Lesotho 310
5.103 Liberia 310
5.104 Libya 311
5.105 Liechtenstein 311
5.106 Lithuania 312
5.107 Luxembourg 312
5.108 Macau 312
5.109 Madagascar 313
5.110 Malawi 313
5.111 Malaysia 314
5.112 Maldives 314
5.113 Mali 315
5.114 Malta 315
5.115 Marshall Islands 315
5.116 Martinique 316
5.117 Mauritania 316
5.118 Mauritius 317
5.119 Mexico 318
5.120 Micronesia Federation 319
5.121 Moldova 319
5.122 Monaco 319
5.123 Mongolia 319
5.124 Morocco 320
5.125 Mozambique 321
5.126 Namibia 321
5.127 Nauru 321
5.128 Nepal 322
5.129 New Caledonia 322
5.130 New Zealand 323
5.131 Nicaragua 323
5.132 Niger 324
5.133 Nigeria 324
5.134 Niue 325
5.135 Norfolk Island 325
5.136 North Korea 325
5.137 Norway 326
5.138 Oman 326
5.139 Pakistan 327
5.140 Palau 327
5.141 Palestine 327
5.142 Panama 328
5.143 Papua New Guinea 328
5.144 Paraguay 329
5.145 Peru 329
5.146 Philippines 330
5.147 Poland 331
5.148 Portugal 331
5.149 Puerto Rico 332
5.150 Qatar 332
5.151 Republic of Congo 333
5.152 Reunion 333
5.153 Romania 334
5.154 Russia 335
5.155 Rwanda 335
5.156 San Marino 336
5.157 Sao Tome E Principe 336
5.158 Saudi Arabia 337
5.159 Senegal 337
5.160 Seychelles 337
5.161 Sierra Leone 338
5.162 Singapore 338
5.163 Slovakia 338
5.164 Slovenia 339
5.165 Solomon Islands 339
5.166 Somalia 340
5.167 South Africa 340
5.168 South Korea 340
5.169 Spain 341
5.170 Sri Lanka 342
5.171 St. Kitts and Nevis 342
5.172 St. Lucia 342
5.173 St. Vincent and the Grenadines 343
5.174 Sudan 343
5.175 Suriname 344
5.176 Swaziland 344
5.177 Sweden 345
5.178 Switzerland 345
5.179 Syrian Arab Republic 346
5.180 Taiwan 347
5.181 Tajikistan 348
5.182 Tanzania 348
5.183 Thailand 349
5.184 The Bahamas 349
5.185 The British Virgin Islands 349
5.186 The Cayman Islands 350
5.187 The Falkland Islands 350
5.188 The Gambia 350
5.189 The Netherlands 351
5.190 The Netherlands Antilles 351
5.191 The Northern Mariana Island 352
5.192 The U.S. Virgin Islands 352
5.193 The United Arab Emirates 352
5.194 The United Kingdom 353
5.195 The United States 354
5.196 Togo 355
5.197 Tokelau 355
5.198 Tonga 356
5.199 Trinidad and Tobago 356
5.200 Tunisia 357
5.201 Turkey 357
5.202 Turkmenistan 358
5.203 Tuvalu 358
5.204 Uganda 358
5.205 Ukraine 359
5.206 Uruguay 360
5.207 Uzbekistan 360
5.208 Vanuatu 361
5.209 Venezuela 362
5.210 Vietnam 363
5.211 Wallis and Futuna 363
5.212 Western Sahara 363
5.213 Western Samoa 364
5.214 Yemen 364
5.215 Zambia 365
5.216 Zimbabwe 365
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 366
6.1 Disclaimers & Safe Harbor 366
6.2 ICON Group International, Inc. User Agreement Provisions 367
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