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The 2009-2014 World Outlook for Snap Servers for Small and Midsize Businesses (SMB)
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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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB). 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 snap servers for small and midsize businesses (SMB). 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 snap servers for small and midsize businesses (SMB).
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 “snap servers for small and midsize businesses (SMB)” 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB) as including all commonly understood products falling within this broad category, such as a network attached storage computer appliance, irrespective of product packaging, formulation, size, or form. Companies participating in this industry include Overland Storage, Adaptec, Appro, Intel, and Xeon. 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB) 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 snap servers for small and midsize businesses (SMB)). 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. |
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Contents: |
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 38
3.36 Nigeria 39
3.37 Republic of Congo 40
3.38 Reunion 41
3.39 Rwanda 42
3.40 Sao Tome E Principe 43
3.41 Senegal 43
3.42 Sierra Leone 44
3.43 Somalia 45
3.44 South Africa 46
3.45 Sudan 47
3.46 Swaziland 48
3.47 Tanzania 49
3.48 The Gambia 50
3.49 Togo 51
3.50 Tunisia 52
3.51 Uganda 53
3.52 Western Sahara 54
3.53 Zambia 54
3.54 Zimbabwe 55
4 ASIA 57
4.1 Executive Summary 57
4.2 Bangladesh 59
4.3 Bhutan 60
4.4 Brunei 61
4.5 Burma 61
4.6 Cambodia 62
4.7 China 63
4.8 Hong Kong 64
4.9 India 64
4.10 Indonesia 65
4.11 Japan 66
4.12 Laos 67
4.13 Macau 68
4.14 Malaysia 69
4.15 Maldives 70
4.16 Mongolia 70
4.17 Nepal 71
4.18 North Korea 72
4.19 Papua New Guinea 73
4.20 Philippines 74
4.21 Seychelles 75
4.22 Singapore 75
4.23 South Korea 76
4.24 Sri Lanka 77
4.25 Taiwan 78
4.26 Thailand 79
4.27 Vietnam 80
5 EUROPE 81
5.1 Executive Summary 81
5.2 Albania 83
5.3 Andorra 84
5.4 Austria 84
5.5 Belarus 85
5.6 Belgium 86
5.7 Bosnia and Herzegovina 87
5.8 Bulgaria 88
5.9 Croatia 89
5.10 Cyprus 90
5.11 Czech Republic 91
5.12 Denmark 92
5.13 Estonia 93
5.14 Finland 94
5.15 France 95
5.16 Georgia 96
5.17 Germany 97
5.18 Greece 98
5.19 Hungary 99
5.20 Iceland 100
5.21 Ireland 101
5.22 Italy 101
5.23 Kazakhstan 102
5.24 Latvia 103
5.25 Liechtenstein 104
5.26 Lithuania 105
5.27 Luxembourg 105
5.28 Malta 106
5.29 Moldova 107
5.30 Monaco 107
5.31 Norway 108
5.32 Poland 109
5.33 Portugal 110
5.34 Romania 111
5.35 Russia 112
5.36 San Marino 113
5.37 Slovakia 113
5.38 Slovenia 114
5.39 Spain 115
5.40 Sweden 116
5.41 Switzerland 117
5.42 The Netherlands 118
5.43 The United Kingdom 119
5.44 Ukraine 120
6 LATIN AMERICA 121
6.1 Executive Summary 121
6.2 Argentina 122
6.3 Belize 123
6.4 Bolivia 124
6.5 Brazil 125
6.6 Chile 126
6.7 Colombia 127
6.8 Costa Rica 128
6.9 Ecuador 129
6.10 El Salvador 130
6.11 French Guiana 131
6.12 Guatemala 131
6.13 Guyana 132
6.14 Honduras 133
6.15 Mexico 134
6.16 Nicaragua 135
6.17 Panama 136
6.18 Paraguay 137
6.19 Peru 138
6.20 Suriname 139
6.21 The Falkland Islands 139
6.22 Uruguay 140
6.23 Venezuela 141
7 NORTH AMERICA & THE CARIBBEAN 142
7.1 Executive Summary 142
7.2 Antigua and Barbuda 144
7.3 Aruba 144
7.4 Barbados 145
7.5 Bermuda 146
7.6 Canada 146
7.7 Cuba 147
7.8 Dominica 148
7.9 Dominican Republic 149
7.10 Greenland 150
7.11 Grenada 151
7.12 Guadeloupe 151
7.13 Haiti 152
7.14 Jamaica 153
7.15 Martinique 154
7.16 Puerto Rico 155
7.17 St. Kitts and Nevis 156
7.18 St. Lucia 156
7.19 St. Vincent and the Grenadines 157
7.20 The Bahamas 158
7.21 The British Virgin Islands 158
7.22 The Cayman Islands 159
7.23 The Netherlands Antilles 160
7.24 The U.S. Virgin Islands 160
7.25 The United States 161
7.26 Trinidad and Tobago 162
8 OCEANA 164
8.1 Executive Summary 164
8.2 American Samoa 166
8.3 Australia 166
8.4 Christmas Island 167
8.5 Cook Islands 168
8.6 Fiji 168
8.7 French Polynesia 169
8.8 Guam 170
8.9 Kiribati 170
8.10 Marshall Islands 171
8.11 Micronesia Federation 172
8.12 Nauru 172
8.13 New Caledonia 173
8.14 New Zealand 174
8.15 Niue 175
8.16 Norfolk Island 175
8.17 Palau 176
8.18 Solomon Islands 177
8.19 The Northern Mariana Island 177
8.20 Tokelau 178
8.21 Tonga 179
8.22 Tuvalu 179
8.23 Vanuatu 180
8.24 Wallis and Futuna 181
8.25 Western Samoa 181
9 THE MIDDLE EAST 183
9.1 Executive Summary 183
9.2 Afghanistan 185
9.3 Armenia 186
9.4 Azerbaijan 187
9.5 Bahrain 188
9.6 Iran 188
9.7 Iraq 189
9.8 Israel 190
9.9 Jordan 191
9.10 Kuwait 192
9.11 Kyrgyzstan 192
9.12 Lebanon 193
9.13 Oman 194
9.14 Pakistan 194
9.15 Palestine 195
9.16 Qatar 196
9.17 Saudi Arabia 196
9.18 Syrian Arab Republic 197
9.19 Tajikistan 198
9.20 The United Arab Emirates 199
9.21 Turkey 200
9.22 Turkmenistan 201
9.23 Uzbekistan 201
9.24 Yemen 202
10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 204
10.1 Disclaimers & Safe Harbor 204
10.2 Icon Group International, Inc. User Agreement Provisions 205
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