The 2009-2014 World Outlook for Rendering Animal Fat, Bones, and Meat Scraps
ICON Group International, September 2008, Pages: 188
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 rendering animal fat, bones, and meat scraps 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. 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 rendering animal fat, bones, and meat scraps 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 rendering animal fat, bones, and meat scraps on a worldwide 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 is 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 rendering animal fat, bones, and meat scraps 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 rendering animal fat, bones, and meat scraps. 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 rendering animal fat, bones, and meat scraps. 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 rendering animal fat, bones, and meat scraps.
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 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, 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 “rendering animal fat, bones, and meat scraps” 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 rendering animal fat, bones, and meat scraps 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 “rendering animal fat, bones, and meat scraps” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of rendering animal fat, bones, and meat scraps, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for rendering animal fat, bones, and meat scraps is 311613. It is for this definition of rendering animal fat, bones, and meat scraps that the aggregate latent demand estimates are derived. “Rendering animal fat, bones, and meat scraps” is specifically defined as follows:
311613
This U.S. industry comprises establishments primarily engaged in rendering animal fat, bones, and meat scraps.
3116131
Animal & marine grease, incl. lard, except canned
31161311
Rendering and meat by_product processing
3116131111
Rendered lard, except canned, not made in meat packing plants
3116131121
Animal and marine grease, other than wool grease
3116131131
Grease, yellow
3116131141
Grease, other (excluding wool grease)
3116134
Animal and marine feed and fertilizer byproducts
31161341
Meat and bone meal feed and fertilizer byproducts
3116134111
Animal and marine meat and bonemeal feed and fertilizer byproducts
31161342
Other feed and fertilizer byproducts
3116134221
Animal and marine dry rendered tankage feed and fertilizer byproducts
3116134231
Animal and marine feather meal feed and fertilizer byproducts
3116134241
Other feed and fertilizer byproducts, including dried blood, poultry fat and byproducts, meal, and raw products for pet food
3116134251
Animal oil mill products, including all other animal oils, except fatty acids
3116134261
Foots, animal oil and acidulated soap stock
3116135
ANIMAL AND MARINE FEED AND FERTILIZER BYPRODUCTS
31161351
Meat and bone meal
3116135111
Meat and bone meal
31161352
Animal and marine feed and fertilizer byproducts, except meat and bone meal
3116135221
Dry rendered tankage
3116135243
Other feed and fertilizer by_products
3116135251
Animal oil mill products, including all other animal oils, except fatty acids
3116135261
Foots, animal oil and acidulated soap stock
3116135271
Foots, vegetable oil
311613M
Miscellaneous receipts
311613P
Primary products
311613S
Secondary products
311613SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 311613 includes the following:
Animal fats rendering
Animal oil rendering
Bones, fat, rendering
Fats, animal, rendering
Grease rendering
Lard made from purchased fat
Meat and bone meal and tankage, produced in rendering plant
Neatsfoot oil rendering
Oil, animal, rendering
Rendering animals (carrion) for feed
Rendering fats
Rendering plants
Stearin, animal, rendering
Tallow produced in rendering plant.
Step 2. Filtering and Smoothing
Based on the aggregate view of rendering animal fat, bones, and meat scraps 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 rendering animal fat, bones, and meat scraps 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 rendering animal fat, bones, and meat scraps 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 rendering animal fat, bones, and meat scraps). 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. I 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 15
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 15
1.3.5 Step 5. Fixed-Parameter Linear Estimation 16
1.3.6 Step 6. Aggregation and Benchmarking 16
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 16
2 SUMMARY OF FINDINGS 18
2.1 The Worldwide Market Potential 18
3 AFRICA, EUROPE & THE MIDDLE EAST 20
3.1 Executive Summary 20
3.2 Afghanistan 21
3.3 Albania 22
3.4 Algeria 23
3.5 Andorra 24
3.6 Angola 24
3.7 Armenia 25
3.8 Austria 26
3.9 Azerbaijan 27
3.10 Bahrain 28
3.11 Belarus 28
3.12 Belgium 29
3.13 Benin 30
3.14 Bosnia and Herzegovina 31
3.15 Botswana 31
3.16 Bulgaria 32
3.17 Burkina Faso 33
3.18 Burundi 34
3.19 Cameroon 34
3.20 Cape Verde 35
3.21 Central African Republic 36
3.22 Chad 36
3.23 Comoros 37
3.24 Congo (formerly Zaire) 38
3.25 Cote dIvoire 39
3.26 Croatia 39
3.27 Cyprus 40
3.28 Czech Republic 41
3.29 Denmark 41
3.30 Djibouti 42
3.31 Egypt 43
3.32 Equatorial Guinea 44
3.33 Estonia 44
3.34 Ethiopia 45
3.35 Finland 46
3.36 France 47
3.37 Gabon 48
3.38 Georgia 48
3.39 Germany 49
3.40 Ghana 50
3.41 Greece 50
3.42 Guinea 51
3.43 Guinea-Bissau 52
3.44 Hungary 52
3.45 Iceland 53
3.46 Iran 54
3.47 Iraq 55
3.48 Ireland 56
3.49 Israel 56
3.50 Italy 57
3.51 Jordan 58
3.52 Kazakhstan 59
3.53 Kenya 60
3.54 Kuwait 61
3.55 Kyrgyzstan 62
3.56 Latvia 62
3.57 Lebanon 63
3.58 Lesotho 64
3.59 Liberia 64
3.60 Libya 65
3.61 Liechtenstein 66
3.62 Lithuania 66
3.63 Luxembourg 67
3.64 Madagascar 68
3.65 Malawi 68
3.66 Mali 69
3.67 Malta 70
3.68 Mauritania 70
3.69 Mauritius 71
3.70 Moldova 72
3.71 Monaco 72
3.72 Morocco 73
3.73 Mozambique 74
3.74 Namibia 74
3.75 Niger 75
3.76 Nigeria 76
3.77 Norway 77
3.78 Oman 77
3.79 Pakistan 78
3.80 Palestine 79
3.81 Poland 79
3.82 Portugal 80
3.83 Qatar 81
3.84 Republic of Congo 81
3.85 Reunion 82
3.86 Romania 83
3.87 Russia 84
3.88 Rwanda 85
3.89 San Marino 85
3.90 Sao Tome E Principe 86
3.91 Saudi Arabia 87
3.92 Senegal 88
3.93 Sierra Leone 88
3.94 Slovakia 89
3.95 Slovenia 90
3.96 Somalia 90
3.97 South Africa 91
3.98 Spain 92
3.99 Sudan 93
3.100 Swaziland 93
3.101 Sweden 94
3.102 Switzerland 95
3.103 Syrian Arab Republic 96
3.104 Tajikistan 97
3.105 Tanzania 98
3.106 The Gambia 98
3.107 The Netherlands 99
3.108 The United Arab Emirates 100
3.109 The United Kingdom 100
3.110 Togo 101
3.111 Tunisia 102
3.112 Turkey 103
3.113 Turkmenistan 104
3.114 Uganda 104
3.115 Ukraine 105
3.116 Uzbekistan 106
3.117 Western Sahara 107
3.118 Yemen 108
3.119 Zambia 108
3.120 Zimbabwe 109
4 ASIA 111
4.1 Executive Summary 111
4.2 Bangladesh 112
4.3 Bhutan 113
4.4 Brunei 114
4.5 Burma 114
4.6 Cambodia 115
4.7 China 116
4.8 Hong Kong 117
4.9 India 117
4.10 Indonesia 118
4.11 Japan 119
4.12 Laos 120
4.13 Macau 121
4.14 Malaysia 122
4.15 Maldives 123
4.16 Mongolia 123
4.17 Nepal 124
4.18 North Korea 125
4.19 Papua New Guinea 126
4.20 Philippines 126
4.21 Seychelles 127
4.22 Singapore 128
4.23 South Korea 128
4.24 Sri Lanka 129
4.25 Taiwan 130
4.26 Thailand 131
4.27 Vietnam 131
5 LATIN AMERICA 133
5.1 Executive Summary 133
5.2 Argentina 134
5.3 Belize 135
5.4 Bolivia 136
5.5 Brazil 136
5.6 Chile 137
5.7 Colombia 138
5.8 Costa Rica 139
5.9 Ecuador 140
5.10 El Salvador 141
5.11 French Guiana 141
5.12 Guatemala 142
5.13 Guyana 143
5.14 Honduras 143
5.15 Mexico 144
5.16 Nicaragua 145
5.17 Panama 146
5.18 Paraguay 147
5.19 Peru 148
5.20 Suriname 149
5.21 The Falkland Islands 149
5.22 Uruguay 150
5.23 Venezuela 151
6 NORTH AMERICA & THE CARIBBEAN 152
6.1 Executive Summary 152
6.2 Antigua and Barbuda 153
6.3 Aruba 154
6.4 Barbados 155
6.5 Bermuda 155
6.6 Canada 156
6.7 Cuba 157
6.8 Dominica 158
6.9 Dominican Republic 158
6.10 Greenland 159
6.11 Grenada 160
6.12 Guadeloupe 161
6.13 Haiti 161
6.14 Jamaica 162
6.15 Martinique 163
6.16 Puerto Rico 163
6.17 St. Kitts and Nevis 164
6.18 St. Lucia 165
6.19 St. Vincent and the Grenadines 165
6.20 The Bahamas 166
6.21 The British Virgin Islands 167
6.22 The Cayman Islands 167
6.23 The Netherlands Antilles 168
6.24 The U.S. Virgin Islands 169
6.25 The United States 169
6.26 Trinidad and Tobago 170
7 OCEANA 171
7.1 Executive Summary 171
7.2 American Samoa 172
7.3 Australia 173
7.4 Christmas Island 174
7.5 Cook Islands 174
7.6 Fiji 175
7.7 French Polynesia 175
7.8 Guam 176
7.9 Kiribati 177
7.10 Marshall Islands 177
7.11 Micronesia Federation 178
7.12 Nauru 178
7.13 New Caledonia 179
7.14 New Zealand 179
7.15 Niue 180
7.16 Norfolk Island 181
7.17 Palau 181
7.18 Solomon Islands 182
7.19 The Northern Mariana Island 182
7.20 Tokelau 183
7.21 Tonga 183
7.22 Tuvalu 184
7.23 Vanuatu 184
7.24 Wallis and Futuna 185
7.25 Western Samoa 185
8 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 187
8.1 Disclaimers & Safe Harbor 187
8.2 ICON Group International, Inc. User Agreement Provisions 188
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