The 2009 Report on Manufacturing Non-Powered Metal Hand and Edge Tools Excluding Saws: World Market Segmentation by City
ICON Group International, May 2009, Pages: 350
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws. 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 non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws. 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 non-powered metal hand and edge tools excluding saws. 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 non-powered metal hand and edge tools excluding saws.
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 non-powered metal hand and edge tools excluding saws” 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 non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing non-powered metal hand and edge tools excluding saws, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing non-powered metal hand and edge tools excluding saws is 332212. It is for this definition of manufacturing non-powered metal hand and edge tools excluding saws that the aggregate latent demand estimates are derived. “Manufacturing non-powered metal hand and edge tools excluding saws” is specifically defined as follows:
332212
This industry comprises establishments primarily engaged in manufacturing nonpowered hand and edge tools (except saws).
3322121
Mechanics hand service tools
33221211
Mechanics’ slip joint pliers
3322121101
Mechanics’ slip joint pliers
332212111
Pliers
33221211112
Slip joint pliers
33221211113
Solid joint pliers
332212112
Ball peen hammers
332212113
Wrenches
33221211321
Sockets, drives, extensions, etc. for hand-operated socket wrenches
33221211322
Open-end wrenches
33221211323
Box wrenches
33221211324
Combination open-end and box wrenches
33221211325
Torque wrenches
33221211326
Adjustable wrenches, including pipe wrenches
33221211327
All other wrenches
332212114
Screwdrivers
332212115
Automotive jacks, mechanical, excluding hydraulic and pneumatic
332212116
Tools for automotive use, excluding jacks
332212117
All other mechanics hand service tools
33221212
Mechanics’ solid joint pliers
3322121206
Mechanics’ solid joint pliers
33221213
Other mechanics’ hand service tools
3322121311
Mechanics’ ball peen hammers
3322121351
Mechanics’ screwdrivers
3322121356
Automobile jacks, mechanical (excluding hydraulic and pneumatic)
3322121361
Mechanics’ tools for automotive use (excluding jacks, but including wheel or gear pullers, valve tools, body or fender tools, etc.)
3322121365
Tape measures
3322121399
Other mechanics’ hand service tools (including blow torches)
33221214
Mechanics’ wrenches
3322121416
Mechanics’ socket wrenches, including sockets, drives (ratchet and other), extensions, etc., for hand~operated socket wrenches
3322121421
Mechanics’ open~end and box wrenches
3322121426
Mechanics’ torque wrenches
3322121431
Mechanics’ adjustable wrenches, including pipe wrenches
3322121436
Mechanics’ combination open~end and box wrenches
3322121444
All other mechanics’ wrenches
3322122
MECHANICS’ HAND SERVICE TOOLS
33221221
Mechanics’ slip joint pliers
3322122101
Mechanics’ slip joint pliers
33221222
Mechanics’ solid joint pliers
3322122206
Mechanics’ solid joint pliers
33221223
Other mechanics’ hand service tools
3322122311
Mechanics’ ball peen hammers
3322122351
Screwdrivers
3322122356
Automobile jacks, mechanical (excluding hydraulic and pneumatic)
3322122361
Mechanics’ tools for automotive use (excluding jacks), including wheel or gear pullers, valve tools, body or fender tools, etc.
3322122398
Other mechanics’ hand service tools (including blow torches and tape measures)
33221224
Mechanics’ wrenches
3322122416
Mechanics’ socket wrenches, including sockets, drives (ratchet and other), extensions, etc.
3322122421
Mechanics’ open_end and box wrenches
3322122426
Mechanics’ torque wrenches
3322122431
Mechanics’ adjustable wrenches, including pipe wrenches
3322122436
Mechanics’ combination open_end and box wrenches
3322122441
Mechanics’ wiring wrenches (including fish wire)
3322122444
All other mechanics’ wrenches
3322123
Edge tools, hand operated
33221231
Other hand~operated edge tools (including agricultural and forestry edge handtools)
3322123101
Axes, adzes, hatchets, and chisels (hand~operated)
3322123106
Professional and craft edge handtools (palette knives, paperhanger knives, putty knives, scrapers, trimmers, etc.)
3322123111
Kitchen hand~operated edge tools (including nonelectric can openers, peelers, slicers, dicers, etc.)
3322123121
Other hand~operated edge tools (including agricultural and forestry edge handtools)
33221232
Hand clippers for animals
3322123216
Hand clippers for animals
33221233
Other hand_operated edge tools (including agricultural and forestry edge handtools)
3322123301
Axes, adzes, hatchets, and chisels (hand_operated)
3322123306
Professional and craft edge handtools (including palette knives, paperhanger knives, putty knives, scrapers, trimmers, etc.)
332212331
Axes, adzes, and hatchets
3322123331
Other hand_operated edge tools (including kitchen, animal hand clippers, agricultural, and forestry edge handtools)
332212383
Chisels
332212385
Professional and craft edge hand tools
332212398
All other edge tools
3322125
Dies and interchangeable cutting tools, for machines and power-driven handtools
33221251
Steel rule dies (except metal cutting), for machines and power_driven handtools
3322125101
Steel rule dies (except metal cutting), for machines and power_driven handtools
33221252
Other cutting dies, for use in cutting cloth, paper, leathers, etc. (excluding dies for cutting metal), for machines & power_driven handtools
3322125206
Other cutting dies, for use in cutting cloth, paper, leathers, etc. (excluding dies for cutting metal), for machines & power_driven handtools
33221253
All other woodcutting machine tools
3322125311
Dies and interchangeable woodcutting tools, for machine and power_driven handtools
3322125316
Machine knives
3322125321
Countersink, drill, and router bits for woodcutting
3322125333
All other woodcutting machine tools (including milling cutters)
332212551
Cutting dies, excluding dies for cutting metal
332212555
Machine knives, except metal cutting
332212559
All other machine tools, including woodcutting
3322127
Other hand tools, n.e.c.
33221271
Other handtools (including woodworking and metal working files and rasps, including precision files, except edge tools)
3322127101
Shovels, spades, scoops, telegraph spoons, and scrapers
332212711
Shovels, spades, scoops, telegraph spoons, and scrapers
3322127111
Light forged hammers, less than 4 lb (excluding ball peen hammers)
3322127116
Heavy forged handtools, sledges (4 lb or more), picks, pick mattocks, and mauls
3322127121
Steel handtool goods (forks, hoes, rakes, weeders, etc.)
3322127131
Soldering irons (electric)
3322127136
Clamps and vises (excluding machine tool accessories)
3322127141
Wheelbarrows
3322127199
Other handtools (including woodworking and metalworking files and rasps, including precision files, except edge tools)
33221272
Nonpowered lawnmowers
332212721
Light forged hammers, under 4 lbs, excluding ball peen hammers
3322127226
Nonpowered lawnmowers
332212731
Heavy forged hammers, sledges ( 4 pounds and over), picks, pick mattocks and mau
332212741
Steel goods, including forks, hoes, rakes, weeders, etc.
332212781
Soldering irons
332212798
Other hand tools, excluding edge and machine tools
3322128
ALL OTHER MISCELLANEOUS HANDTOOLS
33221281
Other handtools, including woodworking and metal working files and rasps, including precision files, (except edge tools)
3322128101
Shovels, spades, scoops, telegraph spoons, and scrapers
3322128111
Light forged hammers, less than 4 lb (excluding ball peen hammers)
3322128116
Heavy forged handtools, sledges (4 lb or more), picks, pick mattocks, and mauls
3322128121
Steel handtool goods (forks, hoes, rakes, weeders, etc.)
3322128131
Soldering irons (electric)
3322128136
Clamps and vises (excluding machine tool accessories)
3322128141
Wheelbarrows
3322128151
Metal cutting shears (including aviation and tinners’ snips, BX, and wire filament cutters)
3322128161
Tool_type scissors and shears
3322128199
Other handtools, including nonpowered lawnmovers, woodworking and metalworking files and rasps, and precision files, (except edge tools)
3322129
Precision measuring tools (inspection, quality control, tool room, and machinist
33221291
Precision measuring tools (inspection, quality control, tool room, and machinists’)
3322129101
Precision measuring tools (inspection, quality control, tool room, and machinists’), comparators (excluding optical)
3322129106
Precision measuring tools (inspection, quality control, tool room, and machinists’), fixture type, fixed size precision measuring limit gauges (American Gauge Design Type C58_61)
3322129111
Precision measuring tools (inspection, quality control, tool room, and machinists’), thread type, fixed size precision measuring limit gauges (American Gauge Design Type C58_61)
3322129116
Precision measuring tools (inspection, quality control, tool room, and machinists’), adjustable size
3322129121
Precision measuring gauge blocks
3322129126
Precision measuring dial indicators
3322129131
Precision measuring micrometers and calipers
3322129146
Other machinists’ precision measuring tools (including dividers, gear checking and surface texture measuring machines)
3322129161
Industrial quality control laser systems and equipment
3322129171
Other precision measuring tools, including industrial quality control laser systems and equipment, dividers, gear checking and surface texture measuring machines
33221292
Pneumatic and electronic precision measuring gauges (manual and automatic)
3322129236
Pneumatic and electronic precision measuring gauges (manual and automatic)
33221293
Coordinate and contour precision measuring machines (inspection and gauging)
3322129341
Coordinate and contour precision measuring machines (inspection and gauging)
33221294
Parts and accessories for machinists’ precision measuring tools (sold separately)
3322129451
Parts and accessories for machinists’ precision measuring tools (sold separately)
332212M
Miscellaneous receipts
332212P
Primary products
332212S
Secondary products
332212SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 332212 includes the following:
Agricultural handtools (e.g., hay forks, hoes, rakes, spades), nonpowered, manufa
Augers, nonpowered, manufacturing
Awls manufacturing
Axes manufacturing
Bearing pullers, handtools, manufacturing
Bits, edge tool, woodworking, manufacturing
Blow torches manufacturing
Calipers and dividers, machinists precision tools, manufacturing
Can openers (except electric) manufacturing
Carpenters handtools, nonelectric (except saws), manufacturing
Caulking guns, nonpowered, manufacturing
C-clamps manufacturing
Chisels manufacturing
Clippers for animal use, nonelectric, manufacturing
Coordinate and contour measuring machines, machinists precision tools, manufactu
Counterbores and countersinking bits, woodworking, manufacturing
Cutters, glass, manufacturing
Cutting dies (e.g., paper, leather, textile) manufacturing
Cutting dies (except metal cutting) manufacturing
Dial indicators, machinists precision tools, manufacturing
Dies, cutting (except metal cutting), manufacturing
Dies, steel rule (except metal cutting), manufacturing
Dividers, machinists precision tools, manufacturing
Drawknives manufacturing
Drill bits, woodworking, manufacturing
Drills, hand held, nonelectric, manufacturing
Edge tools, woodworking (e.g., augers, bits, countersinks), manufacturing
Engravers handtools, nonpowered, manufacturing
Files, handheld, manufacturing
Fish wire (i.e., electrical wiring tool) manufacturing
Forks, handtools (e.g., garden, hay, manure), manufacturing
Gauge blocks, machinists precision tools, manufacturing
Gauges, machinists precision tools (except optical), manufacturing
Gear pullers, handtools, manufacturing
Gouges, woodworking, manufacturing
Grass mowing equipment, nonpowered lawn and garden, manufacturing
Guns, caulking, nonpowered, manufacturing
Hair clippers for animal use, nonelectric, manufacturing
Hammers, handtools, manufacturing
Handheld edge tools (except saws, scissors-type), nonelectric, manufacturing
Handtool metal blades (e.g., putty knives, scrapers, screw drivers) manufacturing
Handtools, machinists precision, manufacturing
Handtools, motor vehicle mechanics, manufacturing
Hatchets manufacturing
Hedge shears and trimmers, nonelectric, manufacturing
Hoes, garden and masons handtools, manufacturing
Hooks, handtools (e.g., baling, bush, grass, husking), manufacturing
Jacks (except hydraulic, pneumatic) manufacturing
Jewelers handtools, nonelectric, manufacturing
Knives and bits for woodworking lathes, planers, and shapers manufacturing
Lawn edgers, nonpowered, manufacturing
Lawnmowers, nonpowered, manufacturing
Leaf skimmers and rakes, nonpowered swimming pool, manufacturing
Levels, carpenters, manufacturing
Machetes manufacturing
Machine knives (except metal cutting) manufacturing
Machinists precision measuring tools (except optical) manufacturing
Mallets (e.g., rubber, wood) manufacturing
Masons handtools manufacturing
Mattocks (i.e., handtools) manufacturing
Mauls, metal, manufacturing
Measuring tools, machinists (except optical), manufacturing
Mechanics handtools, nonpowered, manufacturing
Micrometers, machinists precision tools, manufacturing
Miter boxes manufacturing
Picks (i.e., handtools) manufacturing
Planes, handheld, nonpowered, manufacturing
Pliers, handtools, manufacturing
plumbers handtools, nonpowered, manufacturing
Post hole diggers, nonpowered, manufacturing
Precision tools, machinists (except optical), manufacturing
Pruners manufacturing
Pry (i.e., crow) bars manufacturing
Punches (except paper), nonpowered handtool, manufacturing
Putty knives manufacturing
Rakes, nonpowered handtool, manufacturing
Rasps, handheld, manufacturing
Ratchets, nonpowered, manufacturing
Rulers, metal, manufacturing
Scoops, metal (except kitchen-type), manufacturing
Screw drivers, nonelectric, manufacturing
Screwjacks manufacturing
Scythes manufacturing
Shears, nonelectric, tool-type (e.g., garden, pruners, tinsnip), manufacturing
Shovels, handheld, manufacturing
Sickles manufacturing
Sledgehammers manufacturing
Sockets and socket sets manufacturing
Soldering guns and irons, handheld (including electric), manufacturing
Soldering iron tips and tiplets manufacturing
Spades and shovels, handheld, manufacturing
Squares, carpenters, metal, manufacturing
Stonecutters handtools, nonpowered, manufacturing
Tape measures, metal, manufacturing
Tinners snips manufacturing
Tools, hand, metal blade (e.g., putty knives, scrapers, screwdrivers)
Tools, handheld, nonpowered (except kitchen-type), manufacturing
Tools, woodworking edge (e.g., augers, bits, countersinks), manufacturing
Trimmers, hedge, nonelectric, manufacturing
Trowels manufacturing
Vises (except machine tool attachments) manufacturing
Wheel pullers, handtools, manufacturing
Wrenches, handtools, nonpowered, manufacturing
Yardsticks, metal, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing non-powered metal hand and edge tools excluding saws 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 non-powered metal hand and edge tools excluding saws 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
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 24
1.2.3.3 Step 3. Filling in Missing Values 24
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 24
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 25
1.2.3.6 Step 6. Aggregation and Benchmarking 25
2 USING THE DATA 26
3 CITY SEGMENTS RANKED BY MARKET SIZE 27
3.1 Top 15 Markets 27
3.2 Markets 16 to 30 28
3.3 Remaining Cities by Market Rank 29
4 CITY SEGMENTS IN ALPHABETICAL ORDER 132
4.1 A: from Aalborg to Az Zawiyah 132
4.2 B: from Bacolod to Bydgoszcz 139
4.3 C: from Caaguazu to Cyangugu 147
4.4 D: from Da Nang to Dzhizak 155
4.5 E: from East London to Esteli 159
4.6 F: from Fagatogo to Funchal 161
4.7 G: from Gabes to Gyumri 164
4.8 H: from Hachinohe to Hyderabad 168
4.9 I: from Iasi to Izmir 172
4.10 J: from Jaboatao to Jyvaskyla 175
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 239
4.23 V: from Vacoas-Phoenix to Vukovar 241
4.24 W: from Wadi Medani to Wuhan 244
4.25 X: from Xalapa to Xian 245
4.26 Y: from Yamagata to Yungkang 246
4.27 Z: from Zadar to Zvishavane 247
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 249
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 258
5.25 Botswana 259
5.26 Brazil 260
5.27 Brunei 265
5.28 Bulgaria 265
5.29 Burkina Faso 266
5.30 Burma 266
5.31 Burundi 266
5.32 Cambodia 267
5.33 Cameroon 267
5.34 Canada 268
5.35 Cape Verde 268
5.36 Central African Republic 269
5.37 Chad 269
5.38 Chile 270
5.39 China 270
5.40 Christmas Island 271
5.41 Colombia 271
5.42 Comoros 271
5.43 Congo (formerly Zaire) 272
5.44 Cook Islands 272
5.45 Costa Rica 272
5.46 Cote dIvoire 273
5.47 Croatia 273
5.48 Cuba 274
5.49 Cyprus 274
5.50 Czech Republic 275
5.51 Denmark 275
5.52 Djibouti 276
5.53 Dominica 276
5.54 Dominican Republic 276
5.55 Ecuador 277
5.56 Egypt 277
5.57 El Salvador 278
5.58 Equatorial Guinea 278
5.59 Estonia 278
5.60 Ethiopia 279
5.61 Fiji 279
5.62 Finland 280
5.63 France 280
5.64 French Guiana 281
5.65 French Polynesia 281
5.66 Gabon 281
5.67 Georgia 282
5.68 Germany 282
5.69 Ghana 283
5.70 Greece 283
5.71 Greenland 284
5.72 Grenada 284
5.73 Guadeloupe 285
5.74 Guam 285
5.75 Guatemala 285
5.76 Guinea 286
5.77 Guinea-Bissau 286
5.78 Guyana 286
5.79 Haiti 287
5.80 Honduras 287
5.81 Hong Kong 287
5.82 Hungary 288
5.83 Iceland 288
5.84 India 289
5.85 Indonesia 290
5.86 Iran 291
5.87 Iraq 291
5.88 Ireland 292
5.89 Israel 292
5.90 Italy 293
5.91 Jamaica 293
5.92 Japan 294
5.93 Jordan 297
5.94 Kazakhstan 297
5.95 Kenya 298
5.96 Kiribati 298
5.97 Kuwait 298
5.98 Kyrgyzstan 299
5.99 Laos 299
5.100 Latvia 299
5.101 Lebanon 300
5.102 Lesotho 300
5.103 Liberia 300
5.104 Libya 301
5.105 Liechtenstein 301
5.106 Lithuania 301
5.107 Luxembourg 302
5.108 Macau 302
5.109 Madagascar 302
5.110 Malawi 303
5.111 Malaysia 303
5.112 Maldives 304
5.113 Mali 304
5.114 Malta 304
5.115 Marshall Islands 305
5.116 Martinique 305
5.117 Mauritania 305
5.118 Mauritius 306
5.119 Mexico 307
5.120 Micronesia Federation 308
5.121 Moldova 308
5.122 Monaco 308
5.123 Mongolia 309
5.124 Morocco 309
5.125 Mozambique 310
5.126 Namibia 310
5.127 Nauru 310
5.128 Nepal 311
5.129 New Caledonia 311
5.130 New Zealand 312
5.131 Nicaragua 312
5.132 Niger 313
5.133 Nigeria 313
5.134 Niue 314
5.135 Norfolk Island 314
5.136 North Korea 314
5.137 Norway 315
5.138 Oman 315
5.139 Pakistan 316
5.140 Palau 316
5.141 Palestine 316
5.142 Panama 317
5.143 Papua New Guinea 317
5.144 Paraguay 318
5.145 Peru 318
5.146 Philippines 319
5.147 Poland 319
5.148 Portugal 320
5.149 Puerto Rico 320
5.150 Qatar 321
5.151 Republic of Congo 321
5.152 Reunion 321
5.153 Romania 322
5.154 Russia 322
5.155 Rwanda 323
5.156 San Marino 323
5.157 Sao Tome E Principe 323
5.158 Saudi Arabia 324
5.159 Senegal 324
5.160 Seychelles 325
5.161 Sierra Leone 325
5.162 Singapore 325
5.163 Slovakia 325
5.164 Slovenia 326
5.165 Solomon Islands 326
5.166 Somalia 326
5.167 South Africa 327
5.168 South Korea 327
5.169 Spain 328
5.170 Sri Lanka 328
5.171 St. Kitts and Nevis 329
5.172 St. Lucia 329
5.173 St. Vincent and the Grenadines 329
5.174 Sudan 330
5.175 Suriname 330
5.176 Swaziland 330
5.177 Sweden 331
5.178 Switzerland 331
5.179 Syrian Arab Republic 332
5.180 Taiwan 333
5.181 Tajikistan 334
5.182 Tanzania 334
5.183 Thailand 335
5.184 The Bahamas 335
5.185 The British Virgin Islands 335
5.186 The Cayman Islands 336
5.187 The Falkland Islands 336
5.188 The Gambia 336
5.189 The Netherlands 337
5.190 The Netherlands Antilles 337
5.191 The Northern Mariana Island 337
5.192 The U.S. Virgin Islands 338
5.193 The United Arab Emirates 338
5.194 The United Kingdom 338
5.195 The United States 339
5.196 Togo 340
5.197 Tokelau 340
5.198 Tonga 341
5.199 Trinidad and Tobago 341
5.200 Tunisia 341
5.201 Turkey 342
5.202 Turkmenistan 342
5.203 Tuvalu 342
5.204 Uganda 343
5.205 Ukraine 343
5.206 Uruguay 344
5.207 Uzbekistan 344
5.208 Vanuatu 345
5.209 Venezuela 345
5.210 Vietnam 346
5.211 Wallis and Futuna 346
5.212 Western Sahara 346
5.213 Western Samoa 346
5.214 Yemen 347
5.215 Zambia 347
5.216 Zimbabwe 348
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 349
6.1 Disclaimers & Safe Harbor 349
6.2 ICON Group International, Inc. User Agreement Provisions 350
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