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The 2011 Report on Mixing Pigments, Solvents, and Binders into Paints, Stains, Varnishes, Lacquers, Enamels, Shellacs, and Water-Repellant Coatings and Manufacturing Putties, Paint and Varnish Removers, Paint Brush Cleaners, Frit, and Other Allied Pa
ICON Group International, Jan 2011, Pages: 367
Market Potential Estimation Methodology
Overview
This study covers the world outlook for mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products. 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products. 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products. 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products.
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 “mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products” 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 “mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products The NAICS code for mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products is 325510. It is for this definition of mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products that the aggregate latent demand estimates are derived. “Mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products” is specifically defined as follows:
325510
This industry comprises establishments primarily engaged in (1) mixing pigments, solvents, and binders into paints and other coatings, such as stains, varnishes, lacquers, enamels, shellacs, and water repellant coatings for concrete and masonry, and/or (2) manufacturing allied paint products, such as putties, paint and varnish removers, paint brush cleaners, and frit.
3255101
Architectural coatings, including architectural lacquers
32551010
Architectural coatings
3255101000
Architectural coatings
32551011
Architectural coatings
3255101100
Architectural coatings
3255101111
Architectural coatings, exterior solvent thinned paints and tinting bases, including barn and roof paints
3255101115
Architectural coatings, exterior solvent thinned enamels and tinting bases, including exterior_interior floor enamels
3255101119
Architectural coatings, exterior solvent thinned undercoaters and primers
3255101121
Architectural coatings, exterior solvent thinned clear finishes and sealers
3255101125
Architectural coatings, exterior solvent thinned stains, including shingle and shake
3255101129
Architectural coatings, other exterior solvent thinned coatings, including bituminous paints
3255101131
Architectural coatings, exterior water thinned paints and tinting bases, including barn and roof paints
3255101135
Architectural coatings, exterior water thinned exterior_interior deck and floor enamel
3255101139
Architectural coatings, exterior water thinned undercoaters and primers
3255101141
Architectural coatings, exterior water thinned stains and sealers
3255101145
Architectural coatings, other exterior water thinned coatings
32551012
Architectural coatings, interior
3255101211
Architectural coatings, interior flat solvent thinned wall paint and tinting bases, including mill white paints
3255101215
Architectural coatings, interior gloss and quick drying enamels and other gloss solvent thinned paints and enamels
3255101219
Architectural coatings, interior semigloss, eggshell, satin solvent thinned paints and tinting bases
3255101221
Architectural coatings, interior solvent thinned undercoaters and primers
3255101225
Architectural coatings, interior solvent thinned clear finishes and sealers
3255101229
Architectural coatings, interior solvent thinned stains
3255101231
Architectural coatings, other interior solvent thinned coatings
3255101235
Architectural coatings, interior flat water thinned paints and tinting bases
3255101239
Architectural coatings, interior semigloss, eggshell, satin and other gloss water thinned paints and tinting bases
3255101241
Architectural coatings, interior water thinned undercoaters and primers
3255101245
Architectural coatings, other interior water thinned coatings, stains, and sealers
3255101249
Architectural lacquers
3255101YWV
Architectural coatings, n.s.k
3255102
Product finishes for original equipment manufacturers (OEM), excluding marine coatings
3255103
Special purpose coatings, including all marine coatings
3255104
PRODUCT FINISHES FOR ORIGINAL EQUIPMENT MANUFACTURERS (OEM), EXCLUDING MARINE COATINGS.
32551040
Product finishes for original equipment manufacturers (OEM), excluding marine coatings
3255104000
Product finishes for original equipment manufacturers (OEM), excluding marine coatings
32551041
Product finishes for original equipment manufacturers (OEM), excluding marine coatings.
3255104100
Product finishes for original equipment manufacturers (OEM), excluding marine coatings
3255104111
Automobile, light truck, van, and sport utility vehicle finishes
3255104121
Automobile parts finishes
3255104131
Heavy_duty truck, bus, and recreational vehicle finishes
3255104141
Other transportation equipment finishes, including aircraft and railroad
32551042
Industrial product finishes, except marine coatings
3255104211
Appliance, heating equipment, and air~conditioner finishes
3255104215
Wood furniture, cabinet and fixture finishes
3255104219
Wood and composition board flat stock finishes
3255104221
Metal building product finishes (including coatings for aluminum extrusions and siding)
3255104225
Container and closure finishes
3255104229
Machinery and equipment finishes, including road building equipment and farm implement
3255104231
Nonwood furniture and fixture finishes, including business equipment finishes
3255104235
Paper, paperboard, film, and foil finishes, excluding pigment binders
3255104239
Electrical insulating coatings
3255104241
Thermoset general decorative, appliance powder coatings
3255104245
Thermoset general decorative, automotive powder coatings
3255104249
Thermoset general decorative, architectural powder coatings (such as aluminum extrusions)
3255104251
Thermoset general decorative, lawn and garden powder coatings
3255104255
Thermoset general decorative, general metal finishing powder coatings
3255104259
Thermoset functional powder coatings (for pipe, rebar, electrical insulation, etc.)
3255104261
Thermoplastic powder coatings (all)
3255104265
Other industrial product finishes
3255104YWV
Product finishes for original equipment manufacturers (OEM), excluding marine coatings, n.s.k
3255105
Miscellaneous allied paint products
3255107
SPECIAL_PURPOSE COATINGS INCLUDING ALL MARINE COATINGS, INDUSTRIAL, CONSTRUCTION AND MAINTENANCE COATINGS, TRAFFIC MARKING PAINTS, ETC.
32551070
Special purpose~coatings, including all marine coatings, industrial, construction and maintenance coatings, traffic marking paints, etc.
3255107000
Special purpose~coatings, including all marine coatings, industrial, construction and maintenance coatings, traffic marking paints, etc.
3255107011
Interior industrial new construction and maintenance paints (especially formulated coatings for special conditions of industrial plants and~or facilities requiring protection against extreme temp)
3255107015
Exterior industrial new construction and maintenance paints (especially formulated coatings for special conditions of industrial plants and~or facilities requiring protection against extreme temp)
3255107021
Traffic marking paints (all types, shelf goods and highway department)
3255107031
Automotive, other transportation and machinery refinish paints and enamels, including primers
3255107041
Marine paints, ship and off~shore facilities and shelf goods for both new construction and marine refinish and maintenance (excluding spar varnish)
3255107051
Marine paints for yacht and pleasure craft, new construction, refinish, and maintenance
3255107061
Aerosol ~ paint concentrates produced for packaging in aerosol containers
32551071
Special_purpose coatings including all marine coatings, industrial, construction and maintenance coatings, traffic marking paints, etc.
3255107100
Special_purpose coatings, including all marine coatings, industrial, construction and maintenance coatings, traffic marking paints, etc.
3255107111
Interior industrial new construction and maintenance paints
3255107115
Exterior industrial new construction and maintenance paints
3255107121
Traffic marking paints (all types), shelf goods and highway department
3255107131
Automotive, other transportation, and machinery refinish paints and enamels, including primers
3255107141
Marine paints, ship and off_shore facilities, and shelf goods for both new construction and marine refinish and maintenance (excluding spar varnish)
3255107151
Marine paints for yacht and pleasure craft, new construction, refinish, and maintenance
3255107161
Aerosol (paint concentrates produced for packaging in aerosol containers)
3255107YWV
Special-purpose coatings, n.s.k
325510A
MISCELLANEOUS ALLIED PAINT PRODUCTS (INCLUDING PAINT AND VARNISH REMOVERS, THINNERS, PIGMENT DISPERSIONS, GLAZING COMPOUNDS, ETC.)
325510A0
Miscellaneous allied paint products (including paint and varnish removers, thinners, pigment dispersions, glazing compounds, etc.)
325510A000
Miscellaneous allied paint products (including paint and varnish removers, thinners, pigment dispersions, glazing compounds, etc.)
325510A011
Paint and varnish removers
325510A021
Thinners for lacquers and other solvent based paint products
325510A031
Pigment dispersions
325510A041
Miscellaneous allied paint products (including paint and varnish removers, thinners, pigment dispersions, glazing compounds, etc.)
325510AYWV
Miscellaneous allied paint products, n.s.k
325510B
MISCELLANEOUS ALLIED PAINT PRODUCTS, INCLUDING PAINT AND VARNISH REMOVERS, THINNERS, PIGMENT DISPERSIONS, GLAZING COMPOUNDS, ETC.
325510B1
Miscellaneous allied paint products, including paint and varnish removers, thinners, pigment dispersions, glazing compounds, etc.
325510B100
Miscellaneous allied paint products, including paint and varnish removers, thinners, pigment dispersions, glazing compounds, etc.
325510M
Miscellaneous receipts
325510P
Primary products
325510S
Secondary products
325510SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 325510 includes the following:
Architectural coatings (i.e., paint) manufacturing
Calcimines manufacturing
Dispersions, pigment, manufacturing
Dopes, paint, and laquer, manufacturing
Driers, paint, and varnish, manufacturing
Enamel paints manufacturing
Epoxy coatings made from purchased resins
Fillers, wood (e.g., dry, liquid, paste), manufacturing
Frit manufacturing
Glaziers' putty manufacturing
Industrial product finishes and coatings (i.e., paint) manufacturing
Lacquers manufacturing
Latex paint (i.e., water based) manufacturing
Marine paints manufacturing
Motor vehicle paints manufacturing
Paint and varnish removers manufacturing
Paint thinner and reducer preparations manufacturing
Paintbrush cleaners manufacturing
Paints (except artist's) manufacturing
Paints, emulsion (i.e., latex paint), manufacturing
Paints, oil and alkyd vehicle, manufacturing
Plastic wood fillers manufacturing
Plastisol coating compounds manufacturing
Polyurethane coatings manufacturing
Powder coatings manufacturing
Primers, paint, manufacturing
Shellac manufacturing
Stains (except biological) manufacturing
Varnishes manufacturing
Water repellant coatings for wood, concrete and masonry manufacturing
Wood fillers manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 mixing pigments, solvents, and binders into paints, stains, varnishes, lacquers, enamels, shellacs, and water-repellant coatings and manufacturing putties, paint and varnish removers, paint brush cleaners, frit, and other allied paint products 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 dem
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