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The 2007-2012 World Outlook for Processing or Preserving Meat and Meat Byproducts from Purchased Meats Excluding Poultry and Small Game
ICON Group International, May 2006, Pages: 201
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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game. 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game. 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game.
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 “processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game” 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 “processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game, please see below. The NAICS code for processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game is 311612. It is for this definition of processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game that the aggregate latent demand estimates are derived. “Processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game” is specifically defined as follows:
311612
This U.S. industry comprises establishments primarily engaged in processing or preserving meat and meat byproducts (except poultry and small game) from purchased meats. This industry includes establishments primarily engaged in assembly cutting and packing of meats (i.e., boxed meats) from purchased meats.
3116121
fresh and frozen processed and cured pork made into sausage
31161211
sweet-pickled, dry salt, and dry-cured pork made from purchased carcasses
3116121111
sweet-pickled and dry-cured pork made from purchased carcasses
3116121121
dry salt pork made from purchased carcasses
31161212
smoked hams and picnics made from purchased carcasses
3116121231
smoked hams and picnics made from purchased carcasses
31161213
smoked slab bacon made from purchased carcasses
3116121341
smoked slab bacon made from purchased carcasses
31161214
smoked sliced bacon made from purchased carcasses
3116121451
smoked sliced bacon made from purchased carcasses
31161215
Other smoked pork, not canned or made into sausage, made from purchased carcasses
3116121561
Other smoked pork, not canned or made into sausage, made from purchased carcasses
31161216
boiled ham, barbecue pork, and other cooked pork excluding canned meats and sausage made from purchased carcasses
3116121671
boiled ham, barbecue pork, and other cooked pork made from purchased carcasses excluding canned meats and sausage
31161217
lard made from purchased carcasses
3116121781
lard made from purchased carcasses
3116124
sausage and similar products not made in meat packing plants
31161241
fresh sausage
3116124111
fresh sausage made from purchased carcasses
31161242
dry and semi-dry sausage and similar products
3116124221
dry and semi-dry sausage made from purchased carcasses
31161243
frankfurters and wieners not made in meat packing plants
3116124331
frankfurters and wieners made from purchased carcasses
31161244
Other sausage, smoked or cooked, and jellied goods and similar preparations
3116124441
Other sausage, smoked or cooked (bologna, liverwurst, Polish sausage, etc.) , except canned, made from purchased carcasses
3116124451
headcheese, meat loaves, scrapple, puddings, chili con carne, and jellied goods from purchased carcasses
3116127
canned meats containing at least 20-percent meat excluding dog, cat, and baby food
31161271
canned meats containing at least 20-percent meat made from purchased carcasses excluding dog, cat, and baby food
3116127100
canned meats containing at least 20-percent meat made from purchased carcasses excluding dog, cat, and baby food
311612A
Other processed meats not made in meat packing plants, including boxed meats
311612A1
boxed meat not made in slaughtering plants
311612A111
boxed meat made from purchased carcasses
311612A2
frozen ground meat patties
311612A221
frozen ground meat patties made from purchased carcasses
311612A3
frozen portion-control meats
311612A331
frozen portion control meats made from purchased carcasses
311612A4
Other processed, frozen, or cooked meats, not made in meat packing plants
311612A441
pork rind pellets and pork cracklings made from purchased carcasses
311612A451
Other processed, frozen, or cooked meats (including corned beef, frozen primal and fabricated cuts, frozen variety meats, etc.), made from purchased carcasses
311612A461
collagen sausage casings made from purchased carcasses
311612M
Miscellaneous receipts
311612P
Primary products
311612S
Secondary products
311612SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 311612 includes the following:
Bacon, slab and sliced, made from purchased carcasses
Beef stew made from purchased carcasses
Beef, primal and sub-primal cuts, made from purchased carcasses
Bologna made from purchased carcasses
Boxed beef made from purchased carcasses
Boxed meat produced from purchased carcasses
Collagen sausage casings made from purchased hides
Cooked meats made from purchased carcasses
Corned meats made from purchases carcasses
Cured meats (e.g., brined, dried, and salted) made from purchased carcasses
Dried meats made from purchased carcasses
Frozen meat pies (i.e., tourtires) made from purchased carcasses
Hams, canned, made from purchased carcasses
Hams, preserved (except poultry), made from purchased carcasses
Hot dogs (except poultry) made from purchased carcasses
Lamb, primal and sub-primal cuts, made from purchased carcasses
Luncheon meat (except poultry) made from purchased carcasses
Meat canning (except baby, pet food, poultry), made from purchased carcasses
Meat extracts made from purchased carcasses
Meat products canning (except baby, pet food, poultry) made from purchased carcas
Meats (except poultry), cured or smoked, made from purchased carcasses
Meats, fresh or chilled (except poultry and small game), frozen, made from purch
Pastrami made from purchased carcasses
Pigs feet, cooked and pickled, made from purchased carcasses
Pork, primal and sub-primal cuts, made from purchased carcasses
Potted meats made from purchased carcasses
Processed meats manufacturing
Salted meats made from purchased carcasses
Sandwich spreads, meat, made from purchased carcasses
Sausage and similar cased products made from purchased carcasses
Sausage casings, collagen, made from purchased hides
Smoked meats made from purchased carcasses
Variety meats, edible organs, made from purchased meats
Veal, primal and sub-primal cuts, made from purchased carcasses.
Step 2. Filtering and Smoothing
Based on the aggregate view of processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game 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 processing or preserving meat and meat byproducts from purchased meats excluding poultry and small game). 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.
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