The 2009-2014 World Outlook for Pharmaceutical Vitamin, Nutrient, and Hematinic Preparations
ICON Group International, September 2008, Pages: 189
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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations. 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 pharmaceutical vitamin, nutrient, and hematinic preparations. 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 pharmaceutical vitamin, nutrient, and hematinic preparations.
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 “pharmaceutical vitamin, nutrient, and hematinic preparations” 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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 “pharmaceutical vitamin, nutrient, and hematinic preparations” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of pharmaceutical vitamin, nutrient, and hematinic preparations, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for pharmaceutical vitamin, nutrient, and hematinic preparations is 325412L. It is for this definition of pharmaceutical vitamin, nutrient, and hematinic preparations that the aggregate latent demand estimates are derived. “Pharmaceutical vitamin, nutrient, and hematinic preparations” is specifically defined as follows:
325412L
Pharmaceutical preparations, vitamin, nutrient, and hematinic preparations
325412L0
Pharmaceutical preparations, vitamin, nutrient, and hematinic preparations, for human use
325412L000
Pharmaceutical preparations, vitamin, nutrient, and hematinic preparations, for human use
325412L011
Vitamin preparations, multivitamins, plain and with minerals, except B complex vitamins and fish liver oils
325412L016
Vitamin preparations, pediatric vitamin preparations (drops, suspensions and chewable tablets)
325412L021
Vitamin preparations, prenatal vitamin preparations
325412L026
Vitamin preparations, B complex preparations
325412L031
Vitamin preparations, fluoride preparations
325412L036
Vitamin preparations, nec
325412L041
Fish liver oils
325412L046
Nutrients, excluding therapeutic dietary foods and infant formulas
325412L051
Tonics and alteratives
325412L056
Hematinics, with B12, oral
325412L061
Hematinics, with B12, parenteral
325412L066
Hematinics, except B12, oral
325412L071
Hematinics, except B12, parenteral
325412L076
Hospital solutions (includes dextran, etc., but excludes biologicals such as blood plasma)
325412L081
Pharmaceutical preparations, vitamin, nutrient, and hematinic preparations, for human use, other
325412L1
Pharmaceutical preparations, vitamin, nutrient, and hematinic preparations, for human use
325412L100
Pharmaceutical preparations, vitamin, nutrient, and hematinic preparations, for human use
325412L111
Multivitamins
325412L112
Other vitamins and nutrients
325412L1121
Other vitamins and nutrients, prescription
325412L1122
Other vitamins and nutrients, non-prescription
325412L116
Vitamins, pediatric vitamin preparations (drops, suspensions and chewable tablets)
325412L121
Vitamins, prenatal vitamin preparations
325412L126
Vitamins, B complex preparations
325412L131
Vitamins, fluoride preparations
325412L136
All other vitamin preparations
325412L141
Fish liver oils
325412L146
Nutrients, excluding therapeutic dietary foods and infant formulas
325412L151
Tonics and alteratives
325412L156
Hematinics, with B12, oral
325412L161
Hematinics, with B12, parenteral
325412L166
Hematinics, other (except B12), oral
325412L171
Hematinics, other (except B12), parenteral
325412L176
Hospital solutions (includes dextrose, dextran, etc.), excluding biologicals
325412L181
Other vitamin, nutrient, and hematinic preparations, for human use
Step 2. Filtering and Smoothing
Based on the aggregate view of pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations 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 pharmaceutical vitamin, nutrient, and hematinic preparations). 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 17
2 SUMMARY OF FINDINGS 18
2.1 The Worldwide Market Potential 18
3 AFRICA 20
3.1 Executive Summary 20
3.2 Algeria 21
3.3 Angola 22
3.4 Benin 23
3.5 Botswana 23
3.6 Burkina Faso 24
3.7 Burundi 25
3.8 Cameroon 25
3.9 Cape Verde 26
3.10 Central African Republic 27
3.11 Chad 27
3.12 Comoros 28
3.13 Congo (formerly Zaire) 29
3.14 Cote dIvoire 30
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 34
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 50
3.45 Sudan 51
3.46 Swaziland 52
3.47 Tanzania 52
3.48 The Gambia 53
3.49 Togo 54
3.50 Tunisia 54
3.51 Uganda 55
3.52 Western Sahara 56
3.53 Zambia 56
3.54 Zimbabwe 57
4 ASIA 59
4.1 Executive Summary 59
4.2 Bangladesh 60
4.3 Bhutan 61
4.4 Brunei 62
4.5 Burma 62
4.6 Cambodia 63
4.7 China 64
4.8 Hong Kong 65
4.9 India 65
4.10 Indonesia 66
4.11 Japan 67
4.12 Laos 68
4.13 Macau 69
4.14 Malaysia 70
4.15 Maldives 71
4.16 Mongolia 71
4.17 Nepal 72
4.18 North Korea 73
4.19 Papua New Guinea 74
4.20 Philippines 74
4.21 Seychelles 75
4.22 Singapore 76
4.23 South Korea 76
4.24 Sri Lanka 77
4.25 Taiwan 78
4.26 Thailand 79
4.27 Vietnam 79
5 EUROPE & THE MIDDLE EAST 81
5.1 Executive Summary 81
5.2 Afghanistan 82
5.3 Albania 83
5.4 Andorra 84
5.5 Armenia 84
5.6 Austria 85
5.7 Azerbaijan 86
5.8 Bahrain 87
5.9 Belarus 87
5.10 Belgium 88
5.11 Bosnia and Herzegovina 89
5.12 Bulgaria 90
5.13 Croatia 91
5.14 Cyprus 91
5.15 Czech Republic 92
5.16 Denmark 93
5.17 Estonia 94
5.18 Finland 94
5.19 France 95
5.20 Georgia 96
5.21 Germany 97
5.22 Greece 98
5.23 Hungary 98
5.24 Iceland 99
5.25 Iran 100
5.26 Iraq 101
5.27 Ireland 102
5.28 Israel 102
5.29 Italy 103
5.30 Jordan 104
5.31 Kazakhstan 105
5.32 Kuwait 106
5.33 Kyrgyzstan 107
5.34 Latvia 107
5.35 Lebanon 108
5.36 Liechtenstein 109
5.37 Lithuania 109
5.38 Luxembourg 110
5.39 Malta 111
5.40 Moldova 111
5.41 Monaco 112
5.42 Norway 113
5.43 Oman 113
5.44 Pakistan 114
5.45 Palestine 115
5.46 Poland 115
5.47 Portugal 116
5.48 Qatar 117
5.49 Romania 117
5.50 Russia 118
5.51 San Marino 119
5.52 Saudi Arabia 120
5.53 Slovakia 121
5.54 Slovenia 121
5.55 Spain 122
5.56 Sweden 123
5.57 Switzerland 124
5.58 Syrian Arab Republic 125
5.59 Tajikistan 126
5.60 The Netherlands 127
5.61 The United Arab Emirates 128
5.62 The United Kingdom 128
5.63 Turkey 129
5.64 Turkmenistan 130
5.65 Ukraine 131
5.66 Uzbekistan 132
5.67 Yemen 133
6 LATIN AMERICA 134
6.1 Executive Summary 134
6.2 Argentina 135
6.3 Belize 136
6.4 Bolivia 137
6.5 Brazil 137
6.6 Chile 138
6.7 Colombia 139
6.8 Costa Rica 140
6.9 Ecuador 141
6.10 El Salvador 142
6.11 French Guiana 142
6.12 Guatemala 143
6.13 Guyana 144
6.14 Honduras 144
6.15 Mexico 145
6.16 Nicaragua 146
6.17 Panama 147
6.18 Paraguay 148
6.19 Peru 149
6.20 Suriname 150
6.21 The Falkland Islands 150
6.22 Uruguay 151
6.23 Venezuela 152
7 NORTH AMERICA & THE CARIBBEAN 153
7.1 Executive Summary 153
7.2 Antigua and Barbuda 154
7.3 Aruba 155
7.4 Barbados 156
7.5 Bermuda 156
7.6 Canada 157
7.7 Cuba 158
7.8 Dominica 159
7.9 Dominican Republic 159
7.10 Greenland 160
7.11 Grenada 161
7.12 Guadeloupe 162
7.13 Haiti 162
7.14 Jamaica 163
7.15 Martinique 164
7.16 Puerto Rico 164
7.17 St. Kitts and Nevis 165
7.18 St. Lucia 166
7.19 St. Vincent and the Grenadines 166
7.20 The Bahamas 167
7.21 The British Virgin Islands 168
7.22 The Cayman Islands 168
7.23 The Netherlands Antilles 169
7.24 The U.S. Virgin Islands 170
7.25 The United States 170
7.26 Trinidad and Tobago 171
8 OCEANA 172
8.1 Executive Summary 172
8.2 American Samoa 173
8.3 Australia 174
8.4 Christmas Island 175
8.5 Cook Islands 175
8.6 Fiji 176
8.7 French Polynesia 176
8.8 Guam 177
8.9 Kiribati 178
8.10 Marshall Islands 178
8.11 Micronesia Federation 179
8.12 Nauru 179
8.13 New Caledonia 180
8.14 New Zealand 181
8.15 Niue 182
8.16 Norfolk Island 182
8.17 Palau 183
8.18 Solomon Islands 183
8.19 The Northern Mariana Island 184
8.20 Tokelau 184
8.21 Tonga 185
8.22 Tuvalu 185
8.23 Vanuatu 186
8.24 Wallis and Futuna 186
8.25 Western Samoa 187
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 188
9.1 Disclaimers & Safe Harbor 188
9.2 ICON Group International, Inc. User Agreement Provisions 189
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