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The 2009 Report on Manufacturing Dimension Lumber from Purchased Lumber, Dimension Stock, and Cut Stock; Resawing the Output of Sawmills; and Planing Purchased Lumber: World Market Segmentation by City
ICON Group International, May 2009, Pages: 358
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber. 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber. 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber. 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber.
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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber” 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber is 321912. It is for this definition of manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber that the aggregate latent demand estimates are derived. “Manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber” is specifically defined as follows:
321912
This U.S. industry comprises establishments primarily engaged in one or more of the following: (1) manufacturing dimension lumber from purchased lumber; (2) manufacturing dimension stock (i.e., shapes) or cut stock; (3) resawing the output of sawmills; and (4) planing purchased lumber. These establishments generally use woodworking machinery, such as jointers, planers, lathes, and routers to shape wood.
3219121
Hardwood lumber, not edge worked, made from purchased lumber and edge worked
32191211
Hardwood lumber, made from purchased lumber
3219121111
Beech rough lumber, not edge worked, made from purchased lumber
3219121121
Oak rough lumber, not edge worked, made from purchased lumber
3219121131
Other hardwood rough lumber, not edge worked, made from purchased lumber
3219121141
Hardwood dressed lumber, not edge worked, made from purchased lumber
3219121151
Hardwood lumber, edge worked (tongued, grooved, rabbeted, etc.), made from purchased lumber
3219123
Softwood lumber, not edge worked, made from purchased lumber and edge worked
32191231
Softwood lumber, made from purchased lumber
3219123111
Softwood rough lumber, less than 2 inches in nominal thickness, not edge worked, made from purchased lumber
3219123121
Softwood rough 2_inch lumber, 2 inches in nominal thickness only, not edge worked, made from purchased lumber
3219123131
Softwood rough lumber and timbers, more than 2 inches in nominal thickness, not edge worked, made from purchased lumber
3219123141
Softwood dressed lumber, less than 2 inches in nominal thickness, not edge worked, made from purchased lumber
3219123151
Softwood dressed 2_inch lumber, 2 inches in nominal thickness only, not edge worked, made from purchased lumber
3219123161
Softwood dressed lumber and timbers, more than 2 inches in nominal thickness, not edge worked, made from purchased lumber
3219123171
Softwood lumber, edge worked (tongued, grooved, rabbeted, etc.), made from purchased lumber
3219125
Hardwood cut stock and dimension
32191251
Hardwood furniture cut stock, rough or surfaced, cut to size
3219125111
Hardwood furniture cut stock, rough or surfaced, cut to size, for cabinets
3219125115
Hardwood furniture cut stock, rough or surfaced, cut to size, not for cabinets
32191252
Hardwood furniture dimension, semimachined, including edge and face glued parts
3219125221
Hardwood furniture dimension, semimachined, including edge and face glued parts, for cabinets
3219125225
Hardwood furniture dimension, semimachined, including edge and face glued parts, not for cabinets
32191253
Hardwood furniture dimension, fully machined, ready for assembly
3219125331
Hardwood furniture dimension, fully machined, ready for assembly, for cabinets
3219125335
Hardwood furniture dimension, fully machined, ready for assembly, not for cabinets
32191254
Hardwood industrial cut stock and dimension, and compression~modified or densified wood
3219125441
Hardwood industrial cut stock, rough or surfaced, cut to size
3219125444
Hardwood industrial dimension, semimachined, including edge and face glued parts
3219125447
Hardwood industrial dimension, fully machined, ready for assembly
3219125451
Compression~modified or densified wood (whether or not impregnated with synthetic resin)
3219126
HARDWOOD CUT STOCK AND DIMENSION
32191261
Hardwood furniture cut stock
3219126111
Hardwood furniture cut stock, rough or surfaced, cut to size, for cabinets
3219126115
Hardwood furniture cut stock, rough or surfaced, cut to size, not for cabinets
32191262
Hardwood furniture dimension, semimachined, including edge and face glued parts
3219126221
Hardwood furniture dimension, semimachined, including edge and face glued parts, for cabinets
3219126225
Hardwood furniture dimension, semimachined, including edge and face glued parts, not for cabinets
32191264
Hardwood industrial cut stock, rough or surfaced, cut to size and semi and fully machined, ready for assembly
3219126441
Hardwood industrial cut stock, rough or surfaced, cut to size
3219126449
Hardwood industrial cut stock, semi and fully machined, ready for assembly
3219127
Softwood cut stock and dimension
32191271
Softwood cut stock and dimension
3219127111
Softwood furniture cut stock
3219127121
Softwood industrial cut stock
3219127131
Softwood semimachined and fully machined furniture and industrial dimension
3219128
SOFTWOOD CUT STOCK AND DIMENSION
32191281
Softwood cut stock and dimension
3219128111
Softwood furniture cut stock
3219128121
Softwood industrial cut stock
3219128132
Softwood semimachined and fully machined furniture and industrial dimension
3219129
Sawn wood fence stock, wood lath, and contract resawing and planing
32191291
Sawn wood fence stock, wood lath, and contract resawing and planing
3219129111
Sawn wood fence pickets, posts, and rails not assembled into fence sections
3219129121
Wood lath
3219129131
Receipts for contract resawing and planing
321912M
Miscellaneous receipts
321912P
Primary products
321912S
Secondary products
321912SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 321912 includes the following:
Blanks, wood (e.g., bowling pins, handles, textile machinery accessories), manufa
Boards, wood, resawing purchased lumber
Bobbin blocks and blanks, wood, manufacturing
Bowling pin blanks manufacturing
Brush blocks, wood, turned and shaped
Cants, resawed (lumber), manufacturing
Ceiling lumber, dressed, resawing purchased lumber
Cut stock manufacturing
Dimension lumber, hardwood, resawing purchased lumber
Dimension lumber, resawing purchased lumber
Dimension lumber, softwood, resawing purchased lumber
Dimension stock, hardwood, manufacturing
Dimension stock, softwood, manufacturing
Dimension stock, wood, manufacturing
Furniture dimension stock, hardwood, unfinished, manufacturing
Furniture dimension stock, softwood, unfinished, manufacturing
Furniture dimension stock, unfinished wood, manufacturing
Furniture squares, unfinished hardwood, manufacturing
Gun stock blanks manufacturing
Handle blanks, wood, manufacturing
Handle stock, sawed or planed, manufacturing
Hardwood dimension lumber and stock, resawing purchased lumber
Ladder rounds or rungs, hardwood, manufacturing
Lath, wood, manufacturing
Lathmills, wood
Lumber, hardwood dimension, resawing purchased lumber
Lumber, softwood dimension, resawing purchased lumber
Planing mills (except millwork)
Planing purchased lumber
Resawing purchased lumber
Rounds or rungs, furniture, hardwood, manufacturing
Sawed lumber, resawing purchased lumber
Snow fence lath manufacturing
Softwood dimension lumber and stock, resawing purchased lumber
Stock, chair, unfinished hardwood, manufacturing
Studs, resawing purchased lumber
Turnings, furniture, unfinished wood, manufacturing
Vehicle stock, hardwood, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber 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 dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber is more likely to be similar across cities that have similar characteristics in terms of economic development (i.e., African cities will have similar latent demand structures controlling for the income variation across the pool of African cities).
This approach is useful across cities for which some notion of non-linearity exists in the aggregate consumption function. For some categories, however, the reader must realize that the numbers will reflect a city’s contribution to global latent demand and may never be realized in the form of local sales. For certain category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for cities in “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these cities is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).
Step 5. Fixed-Parameter Linear Estimation
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 2000 cities, there will always be those cities, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these cities, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a city’s stock of income), but a function of current income (a city’s flow of income). In the long run, if a city has no current income, the latent demand for manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities which earn low levels of income will not use their savings, in the long run, to demand manufacturing dimension lumber from purchased lumber, dimension stock, and cut stock; resawing the output of sawmills; and planing purchased lumber). In a graphical sense, for low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income cities are assumed to have a latent demand proportional to their income, based on the city 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 cities 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.
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