The 2009-2014 World Outlook for ASTM Type III, ASTM Type IV, and ASTM Type V Portland Hydraulic Cements
ICON Group International, September 2008, Pages: 195
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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements. 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements. 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements.
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 “ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements” 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 “ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements is 32731003. It is for this definition of ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements that the aggregate latent demand estimates are derived. “ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements” is specifically defined as follows:
32731003
Other portland hydraulic cements including ASTM type III, ASTM type IV, and ASTM type V
3273100311
Portland cement, high early strength ASTM type III, hydraulic (including cost of shipping containers)
3273100321
Portland cement, high sulfate resistance ASTM type V, hydraulic (including cost of shipping containers)
3273100331
Other portland hydraulic cements (oil well, white cement, blended cements, etc.) including low heat of hydration ASTM type IV (including cost of shipping containers)
Step 2. Filtering and Smoothing
Based on the aggregate view of ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements 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 ASTM type III, ASTM type IV, and ASTM type V portland hydraulic cements). 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 14
1.3.3 Step 3. Filling in Missing Values 14
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 14
1.3.5 Step 5. Fixed-Parameter Linear Estimation 15
1.3.6 Step 6. Aggregation and Benchmarking 15
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 15
2 SUMMARY OF FINDINGS 16
2.1 The Worldwide Market Potential 16
3 AFRICA 18
3.1 Executive Summary 18
3.2 Algeria 19
3.3 Angola 20
3.4 Benin 21
3.5 Botswana 21
3.6 Burkina Faso 22
3.7 Burundi 23
3.8 Cameroon 24
3.9 Cape Verde 25
3.10 Central African Republic 25
3.11 Chad 26
3.12 Comoros 27
3.13 Congo (formerly Zaire) 27
3.14 Cote dIvoire 28
3.15 Djibouti 29
3.16 Egypt 30
3.17 Equatorial Guinea 31
3.18 Ethiopia 31
3.19 Gabon 32
3.20 Ghana 33
3.21 Guinea 34
3.22 Guinea-Bissau 34
3.23 Kenya 35
3.24 Lesotho 36
3.25 Liberia 36
3.26 Libya 37
3.27 Madagascar 38
3.28 Malawi 38
3.29 Mali 39
3.30 Mauritania 40
3.31 Mauritius 40
3.32 Morocco 41
3.33 Mozambique 42
3.34 Namibia 42
3.35 Niger 43
3.36 Nigeria 44
3.37 Republic of Congo 45
3.38 Reunion 45
3.39 Rwanda 46
3.40 Sao Tome E Principe 47
3.41 Senegal 47
3.42 Sierra Leone 48
3.43 Somalia 49
3.44 South Africa 49
3.45 Sudan 50
3.46 Swaziland 51
3.47 Tanzania 52
3.48 The Gambia 53
3.49 Togo 54
3.50 Tunisia 55
3.51 Uganda 56
3.52 Western Sahara 56
3.53 Zambia 57
3.54 Zimbabwe 58
4 ASIA & THE MIDDLE EAST 59
4.1 Executive Summary 59
4.2 Afghanistan 60
4.3 Armenia 61
4.4 Azerbaijan 62
4.5 Bahrain 63
4.6 Bangladesh 64
4.7 Bhutan 65
4.8 Brunei 65
4.9 Burma 66
4.10 Cambodia 67
4.11 China 67
4.12 Hong Kong 68
4.13 India 69
4.14 Indonesia 70
4.15 Iran 71
4.16 Iraq 72
4.17 Israel 73
4.18 Japan 73
4.19 Jordan 74
4.20 Kuwait 75
4.21 Kyrgyzstan 76
4.22 Laos 76
4.23 Lebanon 77
4.24 Macau 78
4.25 Malaysia 78
4.26 Maldives 79
4.27 Mongolia 80
4.28 Nepal 80
4.29 North Korea 81
4.30 Oman 82
4.31 Pakistan 82
4.32 Palestine 83
4.33 Papua New Guinea 84
4.34 Philippines 84
4.35 Qatar 85
4.36 Saudi Arabia 86
4.37 Seychelles 87
4.38 Singapore 87
4.39 South Korea 88
4.40 Sri Lanka 89
4.41 Syrian Arab Republic 90
4.42 Taiwan 91
4.43 Tajikistan 92
4.44 Thailand 92
4.45 The United Arab Emirates 93
4.46 Turkey 94
4.47 Turkmenistan 95
4.48 Uzbekistan 95
4.49 Vietnam 96
4.50 Yemen 97
5 EUROPE 98
5.1 Executive Summary 98
5.2 Albania 99
5.3 Andorra 100
5.4 Austria 101
5.5 Belarus 102
5.6 Belgium 103
5.7 Bosnia and Herzegovina 104
5.8 Bulgaria 104
5.9 Croatia 105
5.10 Cyprus 106
5.11 Czech Republic 106
5.12 Denmark 107
5.13 Estonia 108
5.14 Finland 109
5.15 France 110
5.16 Georgia 111
5.17 Germany 111
5.18 Greece 112
5.19 Hungary 113
5.20 Iceland 114
5.21 Ireland 115
5.22 Italy 115
5.23 Kazakhstan 116
5.24 Latvia 117
5.25 Liechtenstein 118
5.26 Lithuania 119
5.27 Luxembourg 119
5.28 Malta 120
5.29 Moldova 121
5.30 Monaco 121
5.31 Norway 122
5.32 Poland 123
5.33 Portugal 124
5.34 Romania 125
5.35 Russia 126
5.36 San Marino 127
5.37 Slovakia 127
5.38 Slovenia 128
5.39 Spain 129
5.40 Sweden 130
5.41 Switzerland 131
5.42 The Netherlands 132
5.43 The United Kingdom 133
5.44 Ukraine 134
6 LATIN AMERICA 135
6.1 Executive Summary 135
6.2 Argentina 136
6.3 Belize 137
6.4 Bolivia 138
6.5 Brazil 139
6.6 Chile 140
6.7 Colombia 141
6.8 Costa Rica 142
6.9 Ecuador 142
6.10 El Salvador 143
6.11 French Guiana 144
6.12 Guatemala 144
6.13 Guyana 145
6.14 Honduras 146
6.15 Mexico 147
6.16 Nicaragua 148
6.17 Panama 149
6.18 Paraguay 149
6.19 Peru 150
6.20 Suriname 151
6.21 The Falkland Islands 152
6.22 Uruguay 152
6.23 Venezuela 153
7 NORTH AMERICA & THE CARIBBEAN 155
7.1 Executive Summary 155
7.2 Antigua and Barbuda 156
7.3 Aruba 157
7.4 Barbados 158
7.5 Bermuda 158
7.6 Canada 159
7.7 Cuba 160
7.8 Dominica 161
7.9 Dominican Republic 161
7.10 Greenland 162
7.11 Grenada 163
7.12 Guadeloupe 164
7.13 Haiti 165
7.14 Jamaica 165
7.15 Martinique 166
7.16 Puerto Rico 167
7.17 St. Kitts and Nevis 168
7.18 St. Lucia 168
7.19 St. Vincent and the Grenadines 169
7.20 The Bahamas 170
7.21 The British Virgin Islands 170
7.22 The Cayman Islands 171
7.23 The Netherlands Antilles 172
7.24 The U.S. Virgin Islands 172
7.25 The United States 173
7.26 Trinidad and Tobago 174
8 OCEANA 176
8.1 Executive Summary 176
8.2 American Samoa 177
8.3 Australia 178
8.4 Christmas Island 179
8.5 Cook Islands 179
8.6 Fiji 180
8.7 French Polynesia 181
8.8 Guam 181
8.9 Kiribati 182
8.10 Marshall Islands 183
8.11 Micronesia Federation 183
8.12 Nauru 184
8.13 New Caledonia 185
8.14 New Zealand 185
8.15 Niue 186
8.16 Norfolk Island 187
8.17 Palau 187
8.18 Solomon Islands 188
8.19 The Northern Mariana Island 189
8.20 Tokelau 189
8.21 Tonga 190
8.22 Tuvalu 191
8.23 Vanuatu 191
8.24 Wallis and Futuna 192
8.25 Western Samoa 193
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 194
9.1 Disclaimers & Safe Harbor 194
9.2 ICON Group International, Inc. User Agreement Provisions 195
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