The 2009-2014 World Outlook for Automated Identification and Susceptibility Systems
ICON Group International, January 2009, Pages: 213
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 automated identification and susceptibility systems is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be 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 automated identification and susceptibility systems 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 automated identification and susceptibility systems 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 automated identification and susceptibility systems 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 automated identification and susceptibility systems. 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 automated identification and susceptibility systems. 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 automated identification and susceptibility systems.
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 larger 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 “automated identification and susceptibility systems” 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 automated identification and susceptibility systems 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, in this report we define the sales of automated identification and susceptibility systems as including all commonly understood products falling within this broad category, such as systems for automated and rapid microbiological tests, irrespective of product packaging, formulation, size, or form. Companies participating in this industry include Abbott Laboratories, Becton Dickinson and Company, bioMerieux, Bio-Rad Laboratories, and Cellabs Pty. In addition to the sources indicated below, additional information available to the public via news and/or press releases published by players in the industry (including reports from AMR Research, Global Industry Analysts, Forrester Research, Frost & Sullivan, Gartner, IDC, and MarketResearch.com) was considered in defining and calibrating this category.
Step 2. Filtering and Smoothing
Based on the aggregate view of automated identification and susceptibility systems 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 automated identification and susceptibility systems 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 automated identification and susceptibility systems 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 automated identification and susceptibility systems). 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 1
1.1 Overview 1
1.2 What is Latent Demand and the P.I.E.? 1
1.3 The Methodology 2
1.3.1 Step 1. Product Definition and Data Collection 4
1.3.2 Step 2. Filtering and Smoothing 5
1.3.3 Step 3. Filling in Missing Values 6
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 6
1.3.5 Step 5. Fixed-Parameter Linear Estimation 7
1.3.6 Step 6. Aggregation and Benchmarking 7
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 7
2 SUMMARY OF FINDINGS 9
2.1 The Worldwide Market Potential 9
3 AFRICA 11
3.1 Executive Summary 11
3.2 Algeria 13
3.3 Angola 14
3.4 Benin 15
3.5 Botswana 15
3.6 Burkina Faso 16
3.7 Burundi 17
3.8 Cameroon 18
3.9 Cape Verde 19
3.10 Central African Republic 19
3.11 Chad 20
3.12 Comoros 21
3.13 Congo (formerly Zaire) 21
3.14 Cote dIvoire 22
3.15 Djibouti 23
3.16 Egypt 24
3.17 Equatorial Guinea 25
3.18 Ethiopia 25
3.19 Gabon 26
3.20 Ghana 27
3.21 Guinea 28
3.22 Guinea-Bissau 28
3.23 Kenya 29
3.24 Lesotho 30
3.25 Liberia 30
3.26 Libya 31
3.27 Madagascar 32
3.28 Malawi 33
3.29 Mali 33
3.30 Mauritania 34
3.31 Mauritius 35
3.32 Morocco 35
3.33 Mozambique 36
3.34 Namibia 37
3.35 Niger 37
3.36 Nigeria 38
3.37 Republic of Congo 39
3.38 Reunion 40
3.39 Rwanda 41
3.40 Sao Tome E Principe 41
3.41 Senegal 42
3.42 Sierra Leone 43
3.43 Somalia 44
3.44 South Africa 45
3.45 Sudan 46
3.46 Swaziland 47
3.47 Tanzania 48
3.48 The Gambia 49
3.49 Togo 50
3.50 Tunisia 51
3.51 Uganda 52
3.52 Western Sahara 53
3.53 Zambia 53
3.54 Zimbabwe 54
4 ASIA 56
4.1 Executive Summary 56
4.2 Bangladesh 58
4.3 Bhutan 59
4.4 Brunei 60
4.5 Burma 60
4.6 Cambodia 61
4.7 China 62
4.8 Hong Kong 63
4.9 India 63
4.10 Indonesia 64
4.11 Japan 65
4.12 Laos 66
4.13 Macau 67
4.14 Malaysia 68
4.15 Maldives 69
4.16 Mongolia 69
4.17 Nepal 70
4.18 North Korea 71
4.19 Papua New Guinea 72
4.20 Philippines 73
4.21 Seychelles 74
4.22 Singapore 74
4.23 South Korea 75
4.24 Sri Lanka 76
4.25 Taiwan 77
4.26 Thailand 78
4.27 Vietnam 79
5 EUROPE 80
5.1 Executive Summary 80
5.2 Albania 82
5.3 Andorra 83
5.4 Austria 83
5.5 Belarus 84
5.6 Belgium 85
5.7 Bosnia and Herzegovina 86
5.8 Bulgaria 87
5.9 Croatia 88
5.10 Cyprus 89
5.11 Czech Republic 89
5.12 Denmark 90
5.13 Estonia 91
5.14 Finland 92
5.15 France 93
5.16 Georgia 94
5.17 Germany 95
5.18 Greece 96
5.19 Hungary 97
5.20 Iceland 98
5.21 Ireland 99
5.22 Italy 99
5.23 Kazakhstan 100
5.24 Latvia 101
5.25 Liechtenstein 102
5.26 Lithuania 103
5.27 Luxembourg 103
5.28 Malta 104
5.29 Moldova 105
5.30 Monaco 105
5.31 Norway 106
5.32 Poland 107
5.33 Portugal 108
5.34 Romania 109
5.35 Russia 110
5.36 San Marino 111
5.37 Slovakia 111
5.38 Slovenia 112
5.39 Spain 113
5.40 Sweden 114
5.41 Switzerland 115
5.42 The Netherlands 116
5.43 The United Kingdom 117
5.44 Ukraine 118
6 LATIN AMERICA 119
6.1 Executive Summary 119
6.2 Argentina 120
6.3 Belize 121
6.4 Bolivia 122
6.5 Brazil 123
6.6 Chile 124
6.7 Colombia 125
6.8 Costa Rica 126
6.9 Ecuador 127
6.10 El Salvador 128
6.11 French Guiana 129
6.12 Guatemala 129
6.13 Guyana 130
6.14 Honduras 131
6.15 Mexico 132
6.16 Nicaragua 133
6.17 Panama 134
6.18 Paraguay 135
6.19 Peru 136
6.20 Suriname 137
6.21 The Falkland Islands 137
6.22 Uruguay 138
6.23 Venezuela 139
7 NORTH AMERICA & THE CARIBBEAN 140
7.1 Executive Summary 140
7.2 Antigua and Barbuda 142
7.3 Aruba 142
7.4 Barbados 143
7.5 Bermuda 144
7.6 Canada 144
7.7 Cuba 145
7.8 Dominica 146
7.9 Dominican Republic 147
7.10 Greenland 148
7.11 Grenada 149
7.12 Guadeloupe 149
7.13 Haiti 150
7.14 Jamaica 151
7.15 Martinique 151
7.16 Puerto Rico 152
7.17 St. Kitts and Nevis 153
7.18 St. Lucia 154
7.19 St. Vincent and the Grenadines 155
7.20 The Bahamas 155
7.21 The British Virgin Islands 156
7.22 The Cayman Islands 157
7.23 The Netherlands Antilles 157
7.24 The U.S. Virgin Islands 158
7.25 The United States 159
7.26 Trinidad and Tobago 160
8 OCEANA 161
8.1 Executive Summary 161
8.2 American Samoa 163
8.3 Australia 163
8.4 Christmas Island 164
8.5 Cook Islands 165
8.6 Fiji 165
8.7 French Polynesia 166
8.8 Guam 167
8.9 Kiribati 167
8.10 Marshall Islands 168
8.11 Micronesia Federation 169
8.12 Nauru 169
8.13 New Caledonia 170
8.14 New Zealand 171
8.15 Niue 172
8.16 Norfolk Island 172
8.17 Palau 173
8.18 Solomon Islands 174
8.19 The Northern Mariana Island 174
8.20 Tokelau 175
8.21 Tonga 176
8.22 Tuvalu 176
8.23 Vanuatu 177
8.24 Wallis and Futuna 178
8.25 Western Samoa 178
9 THE MIDDLE EAST 180
9.1 Executive Summary 180
9.2 Afghanistan 182
9.3 Armenia 183
9.4 Azerbaijan 184
9.5 Bahrain 185
9.6 Iran 185
9.7 Iraq 186
9.8 Israel 187
9.9 Jordan 188
9.10 Kuwait 189
9.11 Kyrgyzstan 189
9.12 Lebanon 190
9.13 Oman 191
9.14 Pakistan 191
9.15 Palestine 192
9.16 Qatar 193
9.17 Saudi Arabia 193
9.18 Syrian Arab Republic 194
9.19 Tajikistan 195
9.20 The United Arab Emirates 196
9.21 Turkey 197
9.22 Turkmenistan 198
9.23 Uzbekistan 198
9.24 Yemen 199
10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 201
10.1 Disclaimers & Safe Harbor 201
10.2 Icon Group International, Inc. User Agreement Provisions 202
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