The 2007-2012 World Outlook for Manufacturing Softwood Veneer and Plywood
ICON Group International, May 2006, Pages: 190
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 softwood veneer and plywood 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 softwood veneer and plywood 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 softwood veneer and plywood 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 softwood veneer and plywood 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 softwood veneer and plywood. 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 softwood veneer and plywood. 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 softwood veneer and plywood.
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 softwood veneer and plywood” 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 softwood veneer and plywood 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 softwood veneer and plywood” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing softwood veneer and plywood, please see below. The NAICS code for manufacturing softwood veneer and plywood is 321212. It is for this definition of manufacturing softwood veneer and plywood that the aggregate latent demand estimates are derived. “Manufacturing softwood veneer and plywood” is specifically defined as follows:
321212
This U.S. industry comprises establishments primarily engaged in manufacturing softwood veneer and/or softwood plywood.
3212121
softwood veneer
32121211
softwood veneer
3212121100
softwood veneer
3212123
interior and exterior rough softwood plywood
32121231
Interior softwood plywood, rough, including touch sanded, C_D exterior glue
3212123111
Interior softwood plywood, rough, including touch sanded, C_D exterior glue
32121232
Interior softwood plywood, rough, including touch sanded, underlayment exterior glue
3212123221
Interior softwood plywood, rough, including touch sanded, underlayment exterior glue
32121233
Other interior softwood plywood, rough, including touch sanded
3212123331
Other interior softwood plywood, rough, including touch sanded
32121234
Exterior softwood plywood, rough, including touch sanded, C ~ C and C ~ C plugged
3212123441
Exterior softwood plywood, rough, including touch sanded, C ~ C
3212123451
Exterior softwood plywood, rough, including touch sanded, C ~ C plugged
32121235
exterior rough softwood plywood
3212123541
Exterior softwood plywood, rough, including touch sanded, C_C
3212123551
Exterior softwood plywood, rough, including touch sanded, C_C plugged
3212123561
Exterior softwood plywood, rough, including touch sanded, other
3212125
sanded interior and exterior softwood plywood
32121251
sanded interior and exterior softwood plywood
3212125111
sanded interior softwood plywood
3212125121
Exterior softwood plywood, sanded, A_C
3212125131
Exterior softwood plywood, sanded, B_B plyform
3212125141
Exterior softwood plywood, sanded, B_C
3212125151
Other exterior softwood plywood, sanded
3212127
Softwood plywood specialties
32121271
Softwood plywood specialties
3212127111
Softwood plywood siding
3212127121
Softwood plywood overlays
3212127191
Other softwood plywood specialties
3212128
softwood plywood specialties
32121281
softwood plywood specialties
3212128111
softwood plywood siding
3212128121
softwood plywood overlays
3212128191
other softwood plywood specialties
3212129
Softwood plywood type products
32121291
Softwood plywood type products
3212129111
Softwood veneered panels, including two~ply veneers
3212129191
Other softwood plywood type products, including cellular panels, and curved and molded plywood
321212M
Miscellaneous receipts
321212P
Primary products
321212S
Secondary products
321212SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 321212 includes the following:
Panels, softwood plywood, manufacturing
Plywood, faced with nonwood materials, softwood, manufacturing
Plywood, softwood faced, manufacturing
Plywood, softwood, manufacturing
Prefinished softwood plywood manufacturing
Softwood plywood composites manufacturing
Softwood veneer or plywood manufacturing
Veneer mills, softwood.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing softwood veneer and plywood 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 softwood veneer and plywood 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 softwood veneer and plywood 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 softwood veneer and plywood). 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 15
1.3.3 Step 3. Filling in Missing Values 16
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16
1.3.5 Step 5. Fixed-Parameter Linear Estimation 16
1.3.6 Step 6. Aggregation and Benchmarking 17
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 17
2 SUMMARY OF FINDINGS 18
2.1 The Worldwide Market Potential 18
3 AFRICA 20
3.1 Executive Summary 20
3.2 Algeria 21
3.3 Angola 22
3.4 Benin 23
3.5 Botswana 23
3.6 Burkina Faso 24
3.7 Burundi 25
3.8 Cameroon 25
3.9 Cape Verde 26
3.10 Central African Republic 27
3.11 Chad 27
3.12 Comoros 28
3.13 Congo (formerly Zaire) 29
3.14 Cote dIvoire 30
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 34
3.22 Guinea-Bissau 35
3.23 Kenya 36
3.24 Lesotho 37
3.25 Liberia 37
3.26 Libya 38
3.27 Madagascar 39
3.28 Malawi 39
3.29 Mali 40
3.30 Mauritania 41
3.31 Mauritius 41
3.32 Morocco 42
3.33 Mozambique 43
3.34 Namibia 43
3.35 Niger 44
3.36 Nigeria 45
3.37 Republic of Congo 46
3.38 Reunion 46
3.39 Rwanda 47
3.40 Sao Tome E Principe 48
3.41 Senegal 48
3.42 Sierra Leone 49
3.43 Somalia 50
3.44 South Africa 50
3.45 Sudan 51
3.46 Swaziland 52
3.47 Tanzania 52
3.48 The Gambia 53
3.49 Togo 54
3.50 Tunisia 54
3.51 Uganda 55
3.52 Western Sahara 56
3.53 Zambia 56
3.54 Zimbabwe 57
4 ASIA 59
4.1 Executive Summary 59
4.2 Bangladesh 60
4.3 Bhutan 61
4.4 Brunei 62
4.5 Burma 62
4.6 Cambodia 63
4.7 China 64
4.8 Hong Kong 65
4.9 India 65
4.10 Indonesia 66
4.11 Japan 67
4.12 Laos 68
4.13 Macau 69
4.14 Malaysia 70
4.15 Maldives 71
4.16 Mongolia 71
4.17 Nepal 72
4.18 North Korea 73
4.19 Papua New Guinea 74
4.20 Philippines 74
4.21 Seychelles 75
4.22 Singapore 76
4.23 South Korea 76
4.24 Sri Lanka 77
4.25 Taiwan 78
4.26 Thailand 79
4.27 Vietnam 79
5 EUROPE 81
5.1 Executive Summary 81
5.2 Albania 82
5.3 Andorra 83
5.4 Austria 83
5.5 Belarus 84
5.6 Belgium 85
5.7 Bosnia and Herzegovina 86
5.8 Bulgaria 87
5.9 Croatia 88
5.10 Cyprus 88
5.11 Czech Republic 89
5.12 Denmark 90
5.13 Estonia 91
5.14 Finland 91
5.15 France 92
5.16 Georgia 93
5.17 Germany 94
5.18 Greece 95
5.19 Hungary 95
5.20 Iceland 96
5.21 Ireland 97
5.22 Italy 97
5.23 Kazakhstan 98
5.24 Latvia 99
5.25 Liechtenstein 100
5.26 Lithuania 101
5.27 Luxembourg 101
5.28 Malta 102
5.29 Moldova 103
5.30 Monaco 103
5.31 Netherlands 104
5.32 Norway 105
5.33 Poland 105
5.34 Portugal 106
5.35 Romania 107
5.36 Russia 108
5.37 San Marino 109
5.38 Slovakia 109
5.39 Slovenia 110
5.40 Spain 111
5.41 Sweden 112
5.42 Switzerland 113
5.43 Ukraine 114
5.44 United Kingdom 115
6 LATIN AMERICA 116
6.1 Executive Summary 116
6.2 Argentina 117
6.3 Belize 118
6.4 Bolivia 119
6.5 Brazil 119
6.6 Chile 120
6.7 Colombia 121
6.8 Costa Rica 122
6.9 Ecuador 123
6.10 El Salvador 124
6.11 Falkland Islands 124
6.12 French Guiana 125
6.13 Guatemala 126
6.14 Guyana 126
6.15 Honduras 127
6.16 Mexico 128
6.17 Nicaragua 129
6.18 Panama 129
6.19 Paraguay 130
6.20 Peru 131
6.21 Suriname 132
6.22 Uruguay 132
6.23 Venezuela 133
7 NORTH AMERICA & THE CARIBBEAN 135
7.1 Executive Summary 135
7.2 Antigua and Barbuda 136
7.3 Aruba 137
7.4 Bahamas 138
7.5 Barbados 138
7.6 Bermuda 139
7.7 British Virgin Islands 140
7.8 Canada 140
7.9 Cayman Islands 141
7.10 Cuba 142
7.11 Dominica 143
7.12 Dominican Republic 143
7.13 Greenland 144
7.14 Grenada 145
7.15 Guadeloupe 146
7.16 Haiti 146
7.17 Jamaica 147
7.18 Martinique 148
7.19 Netherlands Antilles 148
7.20 Puerto Rico 149
7.21 St. Kitts and Nevis 150
7.22 St. Lucia 150
7.23 St. Vincent and the Grenadines 151
7.24 Trinidad and Tobago 152
7.25 United States 152
7.26 Virgin Islands, US 153
8 OCEANA 155
8.1 Executive Summary 155
8.2 American Samoa 156
8.3 Australia 157
8.4 Christmas Island 158
8.5 Cook Islands 158
8.6 Fiji 159
8.7 French Polynesia 159
8.8 Guam 160
8.9 Kiribati 161
8.10 Marshall Islands 161
8.11 Micronesia Federation 162
8.12 Nauru 162
8.13 New Caledonia 163
8.14 New Zealand 163
8.15 Niue 164
8.16 Norfolk Island 165
8.17 Northern Mariana Island 165
8.18 Palau 166
8.19 Solomon Islands 166
8.20 Tokelau 167
8.21 Tonga 167
8.22 Tuvalu 168
8.23 Vanuatu 168
8.24 Wallis and Futuna 169
8.25 Western Samoa 169
9 THE MIDDLE EAST 170
9.1 Executive Summary 170
9.2 Afghanistan 171
9.3 Armenia 172
9.4 Azerbaijan 173
9.5 Bahrain 173
9.6 Iran 174
9.7 Iraq 175
9.8 Israel 176
9.9 Jordan 176
9.10 Kuwait 177
9.11 Kyrgyzstan 178
9.12 Lebanon 178
9.13 Oman 179
9.14 Pakistan 180
9.15 Palestine 181
9.16 Qatar 181
9.17 Saudi Arabia 182
9.18 Syrian Arab Republic 183
9.19 Tajikistan 184
9.20 Turkey 184
9.21 Turkmenistan 185
9.22 United Arab Emirates 186
9.23 Uzbekistan 186
9.24 Yemen 187
10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 189
10.1 Disclaimers & Safe Harbor 189
10.2 User Agreement Provisions 190
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