The 2009-2014 World Outlook for Online Contextual Advertising
ICON Group International, September 2008, Pages: 197
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 online contextual advertising 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 online contextual advertising 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 online contextual advertising 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 online contextual advertising 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 online contextual advertising. 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 online contextual advertising. 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 online contextual advertising.
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 “online contextual advertising” 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 online contextual advertising 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 online contextual advertising as including all commonly understood services falling within this broad category, such as targeted advertisements appearing on websites or other media, such as content displayed in mobile browers, that are selected and served by automated systems based on the content displayed to the user. Companies participating in this industry include ContextWeb, Google, Yahoo!, Microsoft, and Chitika. 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 online contextual advertising 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 online contextual advertising 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 online contextual advertising 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 online contextual advertising). 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 14
3.3 Angola 15
3.4 Benin 16
3.5 Botswana 16
3.6 Burkina Faso 17
3.7 Burundi 18
3.8 Cameroon 18
3.9 Cape Verde 19
3.10 Central African Republic 20
3.11 Chad 20
3.12 Comoros 21
3.13 Congo (formerly Zaire) 22
3.14 Cote dIvoire 23
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 32
3.29 Mali 33
3.30 Mauritania 34
3.31 Mauritius 34
3.32 Morocco 35
3.33 Mozambique 36
3.34 Namibia 36
3.35 Niger 37
3.36 Nigeria 38
3.37 Republic of Congo 39
3.38 Reunion 39
3.39 Rwanda 40
3.40 Sao Tome E Principe 41
3.41 Senegal 41
3.42 Sierra Leone 42
3.43 Somalia 43
3.44 South Africa 43
3.45 Sudan 44
3.46 Swaziland 45
3.47 Tanzania 45
3.48 The Gambia 46
3.49 Togo 47
3.50 Tunisia 48
3.51 Uganda 49
3.52 Western Sahara 49
3.53 Zambia 50
3.54 Zimbabwe 51
4 ASIA & OCEANA 52
4.1 Executive Summary 52
4.2 American Samoa 55
4.3 Australia 56
4.4 Bangladesh 57
4.5 Bhutan 58
4.6 Brunei 58
4.7 Burma 59
4.8 Cambodia 60
4.9 China 60
4.10 Christmas Island 61
4.11 Cook Islands 62
4.12 Fiji 62
4.13 French Polynesia 63
4.14 Guam 64
4.15 Hong Kong 64
4.16 India 65
4.17 Indonesia 66
4.18 Japan 67
4.19 Kiribati 68
4.20 Laos 68
4.21 Macau 69
4.22 Malaysia 70
4.23 Maldives 71
4.24 Marshall Islands 71
4.25 Micronesia Federation 72
4.26 Mongolia 73
4.27 Nauru 73
4.28 Nepal 74
4.29 New Caledonia 75
4.30 New Zealand 75
4.31 Niue 76
4.32 Norfolk Island 77
4.33 North Korea 77
4.34 Palau 78
4.35 Papua New Guinea 79
4.36 Philippines 79
4.37 Seychelles 80
4.38 Singapore 81
4.39 Solomon Islands 81
4.40 South Korea 82
4.41 Sri Lanka 83
4.42 Taiwan 83
4.43 Thailand 84
4.44 The Northern Mariana Island 85
4.45 Tokelau 86
4.46 Tonga 86
4.47 Tuvalu 87
4.48 Vanuatu 88
4.49 Vietnam 88
4.50 Wallis and Futuna 89
4.51 Western Samoa 90
5 EUROPE 91
5.1 Executive Summary 91
5.2 Albania 92
5.3 Andorra 93
5.4 Austria 94
5.5 Belarus 95
5.6 Belgium 96
5.7 Bosnia and Herzegovina 97
5.8 Bulgaria 97
5.9 Croatia 98
5.10 Cyprus 99
5.11 Czech Republic 99
5.12 Denmark 100
5.13 Estonia 101
5.14 Finland 102
5.15 France 103
5.16 Georgia 104
5.17 Germany 104
5.18 Greece 105
5.19 Hungary 106
5.20 Iceland 107
5.21 Ireland 108
5.22 Italy 108
5.23 Kazakhstan 109
5.24 Latvia 110
5.25 Liechtenstein 111
5.26 Lithuania 112
5.27 Luxembourg 112
5.28 Malta 113
5.29 Moldova 114
5.30 Monaco 114
5.31 Norway 115
5.32 Poland 116
5.33 Portugal 117
5.34 Romania 118
5.35 Russia 119
5.36 San Marino 120
5.37 Slovakia 120
5.38 Slovenia 121
5.39 Spain 122
5.40 Sweden 123
5.41 Switzerland 124
5.42 The Netherlands 125
5.43 The United Kingdom 126
5.44 Ukraine 127
6 THE AMERICAS & THE CARIBBEAN 128
6.1 Executive Summary 128
6.2 Antigua and Barbuda 129
6.3 Argentina 130
6.4 Aruba 131
6.5 Barbados 131
6.6 Belize 132
6.7 Bermuda 133
6.8 Bolivia 133
6.9 Brazil 134
6.10 Canada 135
6.11 Chile 136
6.12 Colombia 137
6.13 Costa Rica 138
6.14 Cuba 139
6.15 Dominica 140
6.16 Dominican Republic 140
6.17 Ecuador 141
6.18 El Salvador 142
6.19 French Guiana 142
6.20 Greenland 143
6.21 Grenada 144
6.22 Guadeloupe 144
6.23 Guatemala 145
6.24 Guyana 146
6.25 Haiti 146
6.26 Honduras 147
6.27 Jamaica 148
6.28 Martinique 148
6.29 Mexico 149
6.30 Nicaragua 150
6.31 Panama 151
6.32 Paraguay 152
6.33 Peru 153
6.34 Puerto Rico 154
6.35 St. Kitts and Nevis 155
6.36 St. Lucia 155
6.37 St. Vincent and the Grenadines 156
6.38 Suriname 157
6.39 The Bahamas 157
6.40 The British Virgin Islands 158
6.41 The Cayman Islands 159
6.42 The Falkland Islands 159
6.43 The Netherlands Antilles 160
6.44 The U.S. Virgin Islands 161
6.45 The United States 161
6.46 Trinidad and Tobago 162
6.47 Uruguay 163
6.48 Venezuela 163
7 THE MIDDLE EAST 165
7.1 Executive Summary 165
7.2 Afghanistan 166
7.3 Armenia 167
7.4 Azerbaijan 168
7.5 Bahrain 169
7.6 Iran 170
7.7 Iraq 171
7.8 Israel 172
7.9 Jordan 172
7.10 Kuwait 173
7.11 Kyrgyzstan 174
7.12 Lebanon 174
7.13 Oman 175
7.14 Pakistan 176
7.15 Palestine 177
7.16 Qatar 177
7.17 Saudi Arabia 178
7.18 Syrian Arab Republic 179
7.19 Tajikistan 180
7.20 The United Arab Emirates 180
7.21 Turkey 181
7.22 Turkmenistan 182
7.23 Uzbekistan 182
7.24 Yemen 183
8 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 185
8.1 Disclaimers & Safe Harbor 185
8.2 Icon Group International, Inc. User Agreement Provisions 186
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