The 2007-2012 World Outlook for Manufacturing Sawmill and Woodworking Machinery, Circular and Band Sawing Equipment, Planing Machinery, and Sanding Machinery Excluding Handheld Machinery
ICON Group International, May 2006, Pages: 205
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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 country market.
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 in the introduction, 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery on a worldwide basis, we used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, we 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery across some 230 countries. The smallest have fewer than 10,000 inhabitants. we 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery. So, latent demand in the long-run has a zero intercept. However, we 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, we will now describe the methodology used to create the latent demand estimates for manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery. Since this methodology has been applied 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery.
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, we 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, we 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery” 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery, please see below. The NAICS code for manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery is 333210. It is for this definition of manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery that the aggregate latent demand estimates are derived. “Manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery” is specifically defined as follows:
333210
This industry comprises establishments primarily engaged in manufacturing sawmill and woodworking machinery (except handheld), such as circular and band sawing equipment, planing machinery, and sanding machinery.
3332101
Woodworking machinery including parts, excluding home workshop types
3332102
Woodworking machinery designed primarily for home workshops, incl. parts
3332103
WOODWORKING MACHINERY, INCLUDING PARTS, ATTACHMENTS, AND ACCESSORIES
3332105
WOODWORKING MACHINERY FOR HOME WORKSHOPS, GARAGES, AND SERVICE SHOPS (EXCLUDING CHAINSAWS AND OTHER POWER_DRIVEN HANDTOOLS)
333210M
Miscellaneous receipts
333210P
Primary products
333210S
Secondary products
Furthermore, the definition of NAICS code 333210 includes the following:
Bandsaws, woodworking-type, manufacturing
Circular saws, woodworking-type, stationary, manufacturing
Dovetailing machines, woodworking-type, manufacturing
Drill presses, woodworking-type, manufacturing
Jigsaws, woodworking-type, stationary, manufacturing
Jointers, woodworking-type, manufacturing
Lathes, woodworking-type, manufacturing
Mortisers, woodworking-type, manufacturing
Planers woodworking-type, stationary, manufacturing
Presses for making composite woods (e.g., hardboard, medium density fiberboard (M
Sanding machines, woodworking-type, stationary, manufacturing
Sawmill equipment manufacturing
Saws, bench and table, power-driven, woodworking-type, manufacturing
Scarfing machines, woodworking-type, manufacturing
Shapers, woodworking-type, manufacturing
Veneer and plywood forming machinery manufacturing
Wood verneer laminating and gluing machines manufacturing
Woodworking machines (except handheld) manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 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 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 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.
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”. 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 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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).
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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery). 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.
Step 6. Aggregation and Benchmarking
Based on the models described above, 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.
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 2000 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. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the worlds cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. we allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others.
1 INTRODUCTION 10
1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 14
1.3.3 Step 3. Filling in Missing Values 15
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 15
1.3.5 Step 5. Fixed-Parameter Linear Estimation 16
1.3.6 Step 6. Aggregation and Benchmarking 16
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 16
2 SUMMARY OF FINDINGS 17
2.1 The Worldwide Market Potential 17
3 AFRICA 19
3.1 Executive Summary 19
3.2 Algeria 20
3.3 Angola 21
3.4 Benin 22
3.5 Botswana 23
3.6 Burkina Faso 24
3.7 Burundi 24
3.8 Cameroon 25
3.9 Cape Verde 26
3.10 Central African Republic 26
3.11 Chad 27
3.12 Comoros 28
3.13 Congo (formerly Zaire) 28
3.14 Cote dIvoire 29
3.15 Djibouti 30
3.16 Egypt 31
3.17 Equatorial Guinea 32
3.18 Ethiopia 32
3.19 Gabon 33
3.20 Ghana 34
3.21 Guinea 35
3.22 Guinea-Bissau 35
3.23 Kenya 36
3.24 Lesotho 37
3.25 Liberia 38
3.26 Libya 38
3.27 Madagascar 39
3.28 Malawi 40
3.29 Mali 40
3.30 Mauritania 41
3.31 Mauritius 42
3.32 Morocco 42
3.33 Mozambique 43
3.34 Namibia 44
3.35 Niger 45
3.36 Nigeria 45
3.37 Republic of Congo 46
3.38 Reunion 47
3.39 Rwanda 48
3.40 Sao Tome E Principe 48
3.41 Senegal 49
3.42 Sierra Leone 50
3.43 Somalia 50
3.44 South Africa 51
3.45 Sudan 52
3.46 Swaziland 53
3.47 Tanzania 54
3.48 The Gambia 55
3.49 Togo 56
3.50 Tunisia 57
3.51 Uganda 58
3.52 Western Sahara 59
3.53 Zambia 59
3.54 Zimbabwe 60
4 ASIA 62
4.1 Executive Summary 62
4.2 Bangladesh 63
4.3 Bhutan 64
4.4 Brunei 65
4.5 Burma 66
4.6 Cambodia 67
4.7 China 67
4.8 Hong Kong 68
4.9 India 69
4.10 Indonesia 70
4.11 Japan 71
4.12 Laos 72
4.13 Macau 72
4.14 Malaysia 73
4.15 Maldives 74
4.16 Mongolia 75
4.17 Nepal 75
4.18 North Korea 76
4.19 Papua New Guinea 77
4.20 Philippines 78
4.21 Seychelles 79
4.22 Singapore 80
4.23 South Korea 81
4.24 Sri Lanka 82
4.25 Taiwan 83
4.26 Thailand 84
4.27 Vietnam 85
5 EUROPE 86
5.1 Executive Summary 86
5.2 Albania 87
5.3 Andorra 88
5.4 Austria 89
5.5 Belarus 90
5.6 Belgium 91
5.7 Bosnia and Herzegovina 92
5.8 Bulgaria 93
5.9 Croatia 94
5.10 Cyprus 95
5.11 Czech Republic 95
5.12 Denmark 96
5.13 Estonia 97
5.14 Finland 98
5.15 France 99
5.16 Georgia 100
5.17 Germany 101
5.18 Greece 102
5.19 Hungary 103
5.20 Iceland 104
5.21 Ireland 105
5.22 Italy 105
5.23 Kazakhstan 106
5.24 Latvia 107
5.25 Liechtenstein 108
5.26 Lithuania 109
5.27 Luxembourg 109
5.28 Malta 110
5.29 Moldova 111
5.30 Monaco 111
5.31 Netherlands 112
5.32 Norway 113
5.33 Poland 114
5.34 Portugal 115
5.35 Romania 116
5.36 Russia 117
5.37 San Marino 118
5.38 Slovakia 118
5.39 Slovenia 119
5.40 Spain 120
5.41 Sweden 121
5.42 Switzerland 122
5.43 Ukraine 123
5.44 United Kingdom 124
6 OCEANA 125
6.1 Executive Summary 125
6.2 American Samoa 127
6.3 Australia 127
6.4 Christmas Island 128
6.5 Cook Islands 129
6.6 Fiji 129
6.7 French Polynesia 130
6.8 Guam 131
6.9 Kiribati 131
6.10 Marshall Islands 132
6.11 Micronesia Federation 133
6.12 Nauru 133
6.13 New Caledonia 134
6.14 New Zealand 135
6.15 Niue 136
6.16 Norfolk Island 136
6.17 Northern Mariana Island 137
6.18 Palau 138
6.19 Solomon Islands 138
6.20 Tokelau 139
6.21 Tonga 140
6.22 Tuvalu 140
6.23 Vanuatu 141
6.24 Wallis and Futuna 142
6.25 Western Samoa 142
7 THE AMERICAS 144
7.1 Executive Summary 144
7.2 Antigua and Barbuda 146
7.3 Argentina 146
7.4 Aruba 147
7.5 Bahamas 148
7.6 Barbados 148
7.7 Belize 149
7.8 Bermuda 150
7.9 Bolivia 150
7.10 Brazil 151
7.11 British Virgin Islands 152
7.12 Canada 153
7.13 Cayman Islands 154
7.14 Chile 154
7.15 Colombia 155
7.16 Costa Rica 156
7.17 Cuba 157
7.18 Dominica 158
7.19 Dominican Republic 158
7.20 Ecuador 159
7.21 El Salvador 160
7.22 Falkland Islands 161
7.23 French Guiana 161
7.24 Greenland 162
7.25 Grenada 163
7.26 Guadeloupe 164
7.27 Guatemala 165
7.28 Guyana 166
7.29 Haiti 166
7.30 Honduras 167
7.31 Jamaica 168
7.32 Martinique 168
7.33 Mexico 169
7.34 Netherlands Antilles 170
7.35 Nicaragua 171
7.36 Panama 172
7.37 Paraguay 173
7.38 Peru 174
7.39 Puerto Rico 175
7.40 St. Kitts and Nevis 176
7.41 St. Lucia 176
7.42 St. Vincent and the Grenadines 177
7.43 Suriname 178
7.44 Trinidad and Tobago 178
7.45 United States 179
7.46 Uruguay 180
7.47 Venezuela 181
7.48 Virgin Islands, US 182
8 THE MIDDLE EAST 184
8.1 Executive Summary 184
8.2 Afghanistan 185
8.3 Armenia 186
8.4 Azerbaijan 187
8.5 Bahrain 188
8.6 Iran 189
8.7 Iraq 190
8.8 Israel 191
8.9 Jordan 192
8.10 Kuwait 192
8.11 Kyrgyzstan 193
8.12 Lebanon 194
8.13 Oman 194
8.14 Pakistan 195
8.15 Palestine 196
8.16 Qatar 196
8.17 Saudi Arabia 197
8.18 Syrian Arab Republic 198
8.19 Tajikistan 199
8.20 Turkey 199
8.21 Turkmenistan 200
8.22 United Arab Emirates 201
8.23 Uzbekistan 202
8.24 Yemen 203
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 204
9.1 Disclaimers & Safe Harbor 204
9.2 User Agreement Provisions 205
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