The 2009-2014 World Outlook for Electric Power Transmission, Control, and Distribution
ICON Group International, September 2008, Pages: 190
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 electric power transmission, control, and distribution 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 electric power transmission, control, and distribution 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 electric power transmission, control, and distribution 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 electric power transmission, control, and distribution 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 electric power transmission, control, and distribution. 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 electric power transmission, control, and distribution. 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 electric power transmission, control, and distribution.
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 “electric power transmission, control, and distribution” 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 electric power transmission, control, and distribution 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 “electric power transmission, control, and distribution” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of electric power transmission, control, and distribution, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for electric power transmission, control, and distribution is 22112. It is for this definition of electric power transmission, control, and distribution that the aggregate latent demand estimates are derived. “Electric power transmission, control, and distribution” is specifically defined as follows:
22112
This industry comprises establishments primarily engaged in operating electric power transmission systems, controlling (i.e., regulating voltages) the transmission of electricity, and/or distributing electricity. The transmission system includes lines and transformer stations. These establishments arrange, facilitate, or coordinate the transmission of electricity from the generating source to the distribution centers, other electric utilities, or final consumers. The distribution system consists of lines, poles, meters, and wiring that deliver the electricity to final consumers.
221121
This U.S. industry comprises establishments primarily engaged in operating electric power transmission systems and/or controlling (i.e., regulatory voltage) the transmission of electricity from the generating source to distribution centers or other electric utilities. The transmission system includes lines and transformer stations.
2211211
Establishments primarily engaged in the transmission of electric power from the generating source to the distribution centers. Included are establishments that control the transmission of electric power among electric utilities.
2211212
Establishments primarily engaged in providing electric power transmission in combination with other services, with transmission being the major part though less than 95 percent of the total.
2211213
Electric bulk power transmission and control
221121M
Miscellaneous receipts
221121P
Primary products
221121S
Secondary products
221121SM
Secondary products and miscellaneous receipts
221122
This U.S. industry comprises electric power establishments primarily engaged in either (1) operating electric power distribution systems (i.e., consisting of lines, poles, meters, and wiring) or (2) operating as electric power brokers or agents that arrange the sale of electricity via power distribution systems operated by others.
2211221
Establishments engaged in the distribution of electric power to the final consumer. Included are establishments which both generate and distribute electricity.
2211222
Establishments primarily engaged in providing electric power distribution in combination with other services, with electric distribution being the major part though less than 95 percent of the total.
2211223
Establishments primarily engaged in providing combinations of services with electric power distribution predominating.
22112241
Residential electric power
221122411
New England
221122412
Middle Atlantic
221122413
East North Central
221122414
West North Central
221122415
South Atlantic
221122416
East South Central
221122417
West South Central
221122418
Mountain
221122419
Pacific
22112242
Commercial electric power
221122421
New England
221122422
Middle Atlantic
221122423
East North Central
221122424
West North Central
221122425
South Atlantic
221122426
East South Central
221122427
West South Central
221122428
Mountain
221122429
Pacific
22112243
Industrial electric power
221122431
New England
221122432
Middle Atlantic
221122433
East North Central
221122434
West North Central
221122435
South Atlantic
221122436
East South Central
221122437
West South Central
221122438
Mountain
221122439
Pacific
22112244
Other customers
221122M
Miscellaneous receipts
221122P
Primary products
221122S
Secondary products
221122SM
Secondary products and miscellaneous receipts
Step 2. Filtering and Smoothing
Based on the aggregate view of electric power transmission, control, and distribution 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 electric power transmission, control, and distribution 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 electric power transmission, control, and distribution 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 electric power transmission, control, and distribution). 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 16
1.3.3 Step 3. Filling in Missing Values 16
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16
1.3.5 Step 5. Fixed-Parameter Linear Estimation 17
1.3.6 Step 6. Aggregation and Benchmarking 17
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 17
2 SUMMARY OF FINDINGS 18
2.1 The Worldwide Market Potential 18
3 AFRICA & THE MIDDLE EAST 20
3.1 Executive Summary 20
3.2 Afghanistan 21
3.3 Algeria 22
3.4 Angola 23
3.5 Armenia 24
3.6 Azerbaijan 25
3.7 Bahrain 25
3.8 Benin 26
3.9 Botswana 27
3.10 Burkina Faso 28
3.11 Burundi 28
3.12 Cameroon 29
3.13 Cape Verde 30
3.14 Central African Republic 30
3.15 Chad 31
3.16 Comoros 32
3.17 Congo (formerly Zaire) 32
3.18 Cote dIvoire 33
3.19 Djibouti 34
3.20 Egypt 34
3.21 Equatorial Guinea 35
3.22 Ethiopia 36
3.23 Gabon 37
3.24 Ghana 37
3.25 Guinea 38
3.26 Guinea-Bissau 39
3.27 Iran 39
3.28 Iraq 40
3.29 Israel 41
3.30 Jordan 42
3.31 Kenya 43
3.32 Kuwait 44
3.33 Kyrgyzstan 44
3.34 Lebanon 45
3.35 Lesotho 46
3.36 Liberia 46
3.37 Libya 47
3.38 Madagascar 48
3.39 Malawi 48
3.40 Mali 49
3.41 Mauritania 50
3.42 Mauritius 50
3.43 Morocco 51
3.44 Mozambique 52
3.45 Namibia 52
3.46 Niger 53
3.47 Nigeria 54
3.48 Oman 55
3.49 Pakistan 55
3.50 Palestine 56
3.51 Qatar 57
3.52 Republic of Congo 57
3.53 Reunion 58
3.54 Rwanda 59
3.55 Sao Tome E Principe 59
3.56 Saudi Arabia 60
3.57 Senegal 61
3.58 Sierra Leone 61
3.59 Somalia 62
3.60 South Africa 63
3.61 Sudan 64
3.62 Swaziland 64
3.63 Syrian Arab Republic 65
3.64 Tajikistan 66
3.65 Tanzania 66
3.66 The Gambia 67
3.67 The United Arab Emirates 68
3.68 Togo 68
3.69 Tunisia 69
3.70 Turkey 70
3.71 Turkmenistan 71
3.72 Uganda 71
3.73 Uzbekistan 72
3.74 Western Sahara 73
3.75 Yemen 74
3.76 Zambia 74
3.77 Zimbabwe 75
4 ASIA 77
4.1 Executive Summary 77
4.2 Bangladesh 78
4.3 Bhutan 79
4.4 Brunei 80
4.5 Burma 80
4.6 Cambodia 81
4.7 China 82
4.8 Hong Kong 83
4.9 India 83
4.10 Indonesia 84
4.11 Japan 85
4.12 Laos 86
4.13 Macau 87
4.14 Malaysia 88
4.15 Maldives 89
4.16 Mongolia 89
4.17 Nepal 90
4.18 North Korea 91
4.19 Papua New Guinea 92
4.20 Philippines 92
4.21 Seychelles 93
4.22 Singapore 94
4.23 South Korea 94
4.24 Sri Lanka 95
4.25 Taiwan 96
4.26 Thailand 97
4.27 Vietnam 97
5 EUROPE 99
5.1 Executive Summary 99
5.2 Albania 100
5.3 Andorra 101
5.4 Austria 101
5.5 Belarus 102
5.6 Belgium 103
5.7 Bosnia and Herzegovina 104
5.8 Bulgaria 105
5.9 Croatia 106
5.10 Cyprus 106
5.11 Czech Republic 107
5.12 Denmark 108
5.13 Estonia 109
5.14 Finland 109
5.15 France 110
5.16 Georgia 111
5.17 Germany 112
5.18 Greece 113
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 124
5.35 Russia 125
5.36 San Marino 126
5.37 Slovakia 127
5.38 Slovenia 127
5.39 Spain 128
5.40 Sweden 129
5.41 Switzerland 130
5.42 The Netherlands 131
5.43 The United Kingdom 132
5.44 Ukraine 133
6 LATIN AMERICA 135
6.1 Executive Summary 135
6.2 Argentina 136
6.3 Belize 137
6.4 Bolivia 138
6.5 Brazil 138
6.6 Chile 139
6.7 Colombia 140
6.8 Costa Rica 141
6.9 Ecuador 142
6.10 El Salvador 143
6.11 French Guiana 143
6.12 Guatemala 144
6.13 Guyana 145
6.14 Honduras 145
6.15 Mexico 146
6.16 Nicaragua 147
6.17 Panama 148
6.18 Paraguay 149
6.19 Peru 150
6.20 Suriname 151
6.21 The Falkland Islands 151
6.22 Uruguay 152
6.23 Venezuela 153
7 NORTH AMERICA & THE CARIBBEAN 154
7.1 Executive Summary 154
7.2 Antigua and Barbuda 155
7.3 Aruba 156
7.4 Barbados 157
7.5 Bermuda 157
7.6 Canada 158
7.7 Cuba 159
7.8 Dominica 160
7.9 Dominican Republic 160
7.10 Greenland 161
7.11 Grenada 162
7.12 Guadeloupe 163
7.13 Haiti 163
7.14 Jamaica 164
7.15 Martinique 165
7.16 Puerto Rico 165
7.17 St. Kitts and Nevis 166
7.18 St. Lucia 167
7.19 St. Vincent and the Grenadines 167
7.20 The Bahamas 168
7.21 The British Virgin Islands 169
7.22 The Cayman Islands 169
7.23 The Netherlands Antilles 170
7.24 The U.S. Virgin Islands 171
7.25 The United States 171
7.26 Trinidad and Tobago 172
8 OCEANA 173
8.1 Executive Summary 173
8.2 American Samoa 174
8.3 Australia 175
8.4 Christmas Island 176
8.5 Cook Islands 176
8.6 Fiji 177
8.7 French Polynesia 177
8.8 Guam 178
8.9 Kiribati 179
8.10 Marshall Islands 179
8.11 Micronesia Federation 180
8.12 Nauru 180
8.13 New Caledonia 181
8.14 New Zealand 181
8.15 Niue 182
8.16 Norfolk Island 183
8.17 Palau 183
8.18 Solomon Islands 184
8.19 The Northern Mariana Island 184
8.20 Tokelau 185
8.21 Tonga 185
8.22 Tuvalu 186
8.23 Vanuatu 186
8.24 Wallis and Futuna 187
8.25 Western Samoa 187
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 189
9.1 Disclaimers & Safe Harbor 189
9.2 ICON Group International, Inc. User Agreement Provisions 190
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