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The 2009-2014 World Outlook for Frozen Fruits and Concentrated Fruit Juices, Ades, Drinks, and Non-Alcoholic Cocktails

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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails. 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails. 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails. 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 “frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails” 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 “frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails, please refer to the Web site at The NAICS code for frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails is 3114111. It is for this definition of frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails that the aggregate latent demand estimates are derived. “Frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails” is specifically defined as follows: 3114111 Frozen fruits & concentrated juices, ades, drinks, and nonalcoholic cocktails  31141111 Frozen fruits  3114111111 Frozen strawberries  3114111121 Frozen red sour cherries  3114111131 Frozen apples and applesauce  3114111141 Frozen berries (including blueberries, raspberries, blackberries, etc.)  3114111151 Other frozen fruits  31141112 Frozen concentrated orange juice, consumer and institutional, 6 oz or less  3114111261 Frozen concentrated orange juice, consumer and institutional, 6 oz or less  31141113 Frozen concentrated orange juice, consumer and institutional, 6.1 oz to 12 oz  3114111371 Frozen concentrated orange juice, consumer and institutional, 6.1 oz to 12 oz  31141114 Frozen concentrated orange juice, consumer and institutional, 12.1 oz or more  3114111481 Frozen concentrated orange juice, consumer and institutional, 12.1 oz to 24 oz  3114111491 Frozen concentrated orange juice, consumer and institutional, 24.1 oz or more  31141115 Frozen concentrated orange juice, bulk  31141115A1 Frozen concentrated orange juice, bulk  31141116 Frozen concentrated juices other than orange juice  31141116B1 Frozen concentrated grape juice, 4.1 oz to 7 oz  31141116C1 Frozen concentrated grape juice, 10.1 oz to 13 oz  31141116D1 Frozen concentrated grape juice, other sizes  31141116E1 Frozen concentrated grapefruit juice  31141116F1 Other frozen fruit and berry juices, concentrated  31141116G1 Frozen concentrated lemonade, 4.1 oz to 7 oz  31141116H1 Frozen concentrated lemonade, 10.1 oz to 13 oz  31141116J1 Frozen concentrated lemonade, other sizes  31141116K1 All other frozen concentrated ades, drinks, and cocktails  31141116L1 Frozen citrus pulp  311411179 Frozen concentrated orange juice  311411195 Other frozen concentrated fruit and berry juices  311411197 Citrus pulp and other nonedible citrus byproducts  3114111A Frozen fruits, melons, and berries  3114111B Frozen concentrated fruit and berry juices  3114111C Frozen concentrated fruit ades and nonalcoholic drinks and cocktails   Step 2. Filtering and Smoothing Based on the aggregate view of frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails 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 frozen fruits and concentrated fruit juices, ades, drinks, and non-alcoholic cocktails). 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 17 2.1 The Worldwide Market Potential 17 3 AFRICA 19 3.1 Executive Summary 19 3.2 Algeria 20 3.3 Angola 21 3.4 Benin 22 3.5 Botswana 23 3.6 Burkina Faso 24 3.7 Burundi 24 3.8 Cameroon 25 3.9 Cape Verde 26 3.10 Central African Republic 26 3.11 Chad 27 3.12 Comoros 28 3.13 Congo (formerly Zaire) 28 3.14 Cote dIvoire 29 3.15 Djibouti 30 3.16 Egypt 31 3.17 Equatorial Guinea 32 3.18 Ethiopia 32 3.19 Gabon 33 3.20 Ghana 34 3.21 Guinea 35 3.22 Guinea-Bissau 35 3.23 Kenya 36 3.24 Lesotho 37 3.25 Liberia 37 3.26 Libya 38 3.27 Madagascar 39 3.28 Malawi 39 3.29 Mali 40 3.30 Mauritania 41 3.31 Mauritius 41 3.32 Morocco 42 3.33 Mozambique 43 3.34 Namibia 43 3.35 Niger 44 3.36 Nigeria 45 3.37 Republic of Congo 46 3.38 Reunion 46 3.39 Rwanda 47 3.40 Sao Tome E Principe 48 3.41 Senegal 48 3.42 Sierra Leone 49 3.43 Somalia 50 3.44 South Africa 51 3.45 Sudan 52 3.46 Swaziland 53 3.47 Tanzania 53 3.48 The Gambia 54 3.49 Togo 55 3.50 Tunisia 56 3.51 Uganda 57 3.52 Western Sahara 58 3.53 Zambia 58 3.54 Zimbabwe 59 4 ASIA 61 4.1 Executive Summary 61 4.2 Bangladesh 62 4.3 Bhutan 63 4.4 Brunei 64 4.5 Burma 65 4.6 Cambodia 66 4.7 China 66 4.8 Hong Kong 67 4.9 India 68 4.10 Indonesia 69 4.11 Japan 70 4.12 Laos 71 4.13 Macau 71 4.14 Malaysia 72 4.15 Maldives 73 4.16 Mongolia 74 4.17 Nepal 74 4.18 North Korea 75 4.19 Papua New Guinea 76 4.20 Philippines 76 4.21 Seychelles 77 4.22 Singapore 78 4.23 South Korea 79 4.24 Sri Lanka 80 4.25 Taiwan 81 4.26 Thailand 82 4.27 Vietnam 83 5 EUROPE & THE MIDDLE EAST 84 5.1 Executive Summary 84 5.2 Afghanistan 85 5.3 Albania 86 5.4 Andorra 87 5.5 Armenia 88 5.6 Austria 89 5.7 Azerbaijan 90 5.8 Bahrain 91 5.9 Belarus 91 5.10 Belgium 92 5.11 Bosnia and Herzegovina 93 5.12 Bulgaria 94 5.13 Croatia 95 5.14 Cyprus 96 5.15 Czech Republic 96 5.16 Denmark 97 5.17 Estonia 98 5.18 Finland 99 5.19 France 100 5.20 Georgia 101 5.21 Germany 102 5.22 Greece 103 5.23 Hungary 104 5.24 Iceland 105 5.25 Iran 106 5.26 Iraq 107 5.27 Ireland 108 5.28 Israel 108 5.29 Italy 109 5.30 Jordan 110 5.31 Kazakhstan 111 5.32 Kuwait 112 5.33 Kyrgyzstan 113 5.34 Latvia 114 5.35 Lebanon 114 5.36 Liechtenstein 115 5.37 Lithuania 116 5.38 Luxembourg 116 5.39 Malta 117 5.40 Moldova 118 5.41 Monaco 118 5.42 Norway 119 5.43 Oman 120 5.44 Pakistan 120 5.45 Palestine 121 5.46 Poland 122 5.47 Portugal 123 5.48 Qatar 124 5.49 Romania 124 5.50 Russia 125 5.51 San Marino 126 5.52 Saudi Arabia 127 5.53 Slovakia 128 5.54 Slovenia 128 5.55 Spain 129 5.56 Sweden 130 5.57 Switzerland 131 5.58 Syrian Arab Republic 132 5.59 Tajikistan 133 5.60 The Netherlands 134 5.61 The United Arab Emirates 135 5.62 The United Kingdom 135 5.63 Turkey 136 5.64 Turkmenistan 137 5.65 Ukraine 138 5.66 Uzbekistan 139 5.67 Yemen 140 6 LATIN AMERICA 142 6.1 Executive Summary 142 6.2 Argentina 143 6.3 Belize 144 6.4 Bolivia 145 6.5 Brazil 146 6.6 Chile 147 6.7 Colombia 148 6.8 Costa Rica 149 6.9 Ecuador 149 6.10 El Salvador 150 6.11 French Guiana 151 6.12 Guatemala 151 6.13 Guyana 152 6.14 Honduras 153 6.15 Mexico 154 6.16 Nicaragua 155 6.17 Panama 156 6.18 Paraguay 157 6.19 Peru 158 6.20 Suriname 159 6.21 The Falkland Islands 159 6.22 Uruguay 160 6.23 Venezuela 161 7 NORTH AMERICA & THE CARIBBEAN 162 7.1 Executive Summary 162 7.2 Antigua and Barbuda 163 7.3 Aruba 164 7.4 Barbados 165 7.5 Bermuda 165 7.6 Canada 166 7.7 Cuba 167 7.8 Dominica 168 7.9 Dominican Republic 168 7.10 Greenland 169 7.11 Grenada 170 7.12 Guadeloupe 171 7.13 Haiti 172 7.14 Jamaica 172 7.15 Martinique 173 7.16 Puerto Rico 174 7.17 St. Kitts and Nevis 175 7.18 St. Lucia 175 7.19 St. Vincent and the Grenadines 176 7.20 The Bahamas 177 7.21 The British Virgin Islands 177 7.22 The Cayman Islands 178 7.23 The Netherlands Antilles 179 7.24 The U.S. Virgin Islands 179 7.25 The United States 180 7.26 Trinidad and Tobago 181 8 OCEANA 183 8.1 Executive Summary 183 8.2 American Samoa 184 8.3 Australia 185 8.4 Christmas Island 186 8.5 Cook Islands 186 8.6 Fiji 187 8.7 French Polynesia 188 8.8 Guam 188 8.9 Kiribati 189 8.10 Marshall Islands 190 8.11 Micronesia Federation 190 8.12 Nauru 191 8.13 New Caledonia 192 8.14 New Zealand 192 8.15 Niue 193 8.16 Norfolk Island 194 8.17 Palau 194 8.18 Solomon Islands 195 8.19 The Northern Mariana Island 196 8.20 Tokelau 196 8.21 Tonga 197 8.22 Tuvalu 198 8.23 Vanuatu 198 8.24 Wallis and Futuna 199 8.25 Western Samoa 200 9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 201 9.1 Disclaimers & Safe Harbor 201 9.2 ICON Group International, Inc. User Agreement Provisions 202
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