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The 2019-2024 World Outlook for Manufacturing Non-Powered Metal Hand and Edge Tools Excluding Saws

The 2019-2024 World Outlook for Manufacturing Non-Powered Metal Hand and Edge Tools Excluding Saws

  • January 2018
  • 290 pages
  • ID: 1993210

Summary

Table of Contents

This study covers the world outlook for manufacturing non-powered metal hand and edge tools excluding saws across more than 190 countries. For each year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the country in question (in millions of U.S. dollars), the percent share the country is of the region, and of the globe.
 
These comparative benchmarks allow the reader to quickly gauge a country 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 countries 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 regional and national markets. For each country, I also show my estimates of how the P.I.E. grows over time (positive or negative growth). 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.
 
1.3 THE METHODOLOGY
In order to estimate the latent demand for manufacturing non-powered metal hand and edge tools excluding saws on a worldwide 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 is 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 shown as "B" 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. For a general overview of this subject area, see Principles of Macroeconomics by N. Gregory Mankiw, South-Western College Publishing; ISBN: 0030340594; 2nd edition (February 2002).

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 190 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.

1.3.1 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 are more likely to be at or near efficiency than others.

These countries are given greater weight than others in the estimation of latent demand compared to other countries 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 cannot be 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 have 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.

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 190 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.

1.3.1 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 are more likely to be at or near efficiency than others.

These countries are given greater weight than others in the estimation of latent demand compared to other countries 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 cannot be 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 have 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 products and/or services that might be incorporated within manufacturing non-powered metal hand and edge tools excluding saws fall 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 countries 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").

The NAICS code for manufacturing non-powered metal hand and edge tools excluding saws is 332212. It is for this definition that 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

This report was prepared from a variety of sources including excerpts from documents and official reports or databases published by the World Bank, the U.S. Department of Commerce, the U.S. State Department, various national agencies, the International Monetary Fund, the Central Intelligence Agency, various agencies from the United Nations (e.g. ILO, ITU, UNDP, etc.), and non-governmental sources, including ICON Group Ltd., Euromonitor, the World Resources Institute, Mintel, the U.S. Industrial Outlook, and various public sources cited in the trade press.

1.3.2 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 as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries 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 a 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.

1.3.3 STEP 3. FILLING IN MISSING VALUES
In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year.

From a Bayesian perspective, these observations should be given the 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), countries which have missing data for any given year are estimated based on historical dynamics of aggregate income for that country.

1.3.4 STEP 4. VARYING PARAMETER, NON-LINEAR ESTIMATION
Given the data available from the first three steps, the latent demand in additional countries is estimated using a "varying-parameter cross-sectionally pooled time series model".

The interested reader can find longer discussions of this type of modeling in Studies in Global Econometrics (Advanced Studies in Theoretical and Applied Econometrics V. 30), by Henri Theil, et al., Kluwer Academic Publishers; ISBN: 0792336607; (June 1996), and in Principles of Econometrics, by Henri Theil John Wiley & Sons; ISBN: 0471858455; (December 1971), and in Econometric Models and Economic Forecasts by Robert S. Pindyck, Daniel L. Rubinfeld McGraw Hill Text; ISBN: 0070500983; 3rd edition (December 1991). Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily the same for all countries). This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country 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 countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries). This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function.

For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-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 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 countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).

1.3.5 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 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible.

For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for manufacturing non-powered metal hand and edge tools excluding saws is assumed to approach zero.

The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand manufacturing non-powered metal hand and edge tools excluding saws). In a graphical sense, for low-income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function.

1.3.6 STEP 6. AGGREGATION AND BENCHMARKING
Based on the models described in Chapter 1, latent demand figures are estimated for all countries 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.

1.3.7 STEP 7. LATENT DEMAND DENSITY: ALLOCATING ACROSS CITIES 
With the advent of a "borderless world", cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2,000 cities. 
 
The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. 
 
To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another. 
 
Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. 

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Purchase Reports From Reputable Market Research Publishers
2018 Future of Global Staple Gun Market to 2025- Growth Opportunities, Competition, Trends and Outlook of Staple Gun Across Applications and Regions Report

2018 Future of Global Staple Gun Market to 2025- Growth Opportunities, Competition, Trends and Outlook of Staple Gun Across Applications and Regions Report

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The global demand for Staple Gun is forecast to report strong growth driven by consumption in major emerging markets.More growth opportunities will turn up between 2018 and 2025 as compared to the past ...

Global Hand Tools Market 2018-2022

Hand Tools Sector: Worldwide Forecast until 2022

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About Hand ToolsHand tools is an umbrella term for instruments such as pliers, cutters, and wrenches that are not powered by electricity. Hand tools are used in activities such as chopping, chiseling, ...

Hand Tool & Cutlery Manufacturing in Canada - Industry Market Research Report

Hand Tool & Cutlery Manufacturing in Canada - Industry Market Research Report

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Dull blade: Revenue will contract as input prices rise and competition remains fierceAbstractHand Tool & Cutlery Manufacturing in CanadaThe Hand Tool and Cutlery Manufacturing industry in Canada produces ...


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