The 2016-2021 World Outlook for 2 to 5 Megawatt Engines

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 13
1.3.3 Step 3. Filling in Missing Values 13
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 13
1.3.5 Step 5. Fixed-Parameter Linear Estimation 14
1.3.6 Step 6. Aggregation and Benchmarking 14
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 14
1.4 Frequently Asked Questions (FAQ) 15
1.4.1 Category Definition 15
1.4.2 Units 15
1.4.3 Methodology 16
2.1 The Worldwide Market Potential 18
3.1 Executive Summary 20
3.2 Afghanistan 22
3.3 Albania 23
3.4 Algeria 23
3.5 Andorra 25
3.6 Angola 25
3.7 Armenia 26
3.8 Austria 27
3.9 Azerbaijan 28
3.10 Bahrain 29
3.11 Belarus 30
3.12 Belgium 31
3.13 Benin 32
3.14 Bosnia and Herzegovina 33
3.15 Botswana 33
3.16 Bulgaria 34
3.17 Burkina Faso 35
3.18 Burundi 36
3.19 Cameroon 37
3.20 Cape Verde 38
3.21 Central African Republic 38
3.22 Chad 39
3.23 Comoros 40
3.24 Congo (formerly Zaire) 41
3.25 Cote d'Ivoire 42
3.26 Croatia 42
3.27 Cyprus 43
3.28 Czech Republic 44
3.29 Denmark 45
3.30 Djibouti 46
3.31 Egypt 46
3.32 Equatorial Guinea 47
3.33 Eritrea 48
3.34 Estonia 49
3.35 Ethiopia 49
3.36 Finland 50
3.37 France 51
3.38 Gabon 53
3.39 Georgia 53
3.40 Germany 54
3.41 Ghana 55
3.42 Greece 57
3.43 Guinea 59
3.44 Guinea-Bissau 59
3.45 Hungary 60
3.46 Iceland 61
3.47 Iran 62
3.48 Iraq 65
3.49 Ireland 66
3.50 Israel 66
3.51 Italy 67
3.52 Jordan 69
3.53 Kazakhstan 69
3.54 Kenya 71
3.55 Kosovo 73
3.56 Kuwait 73
3.57 Kyrgyzstan 74
3.58 Latvia 75
3.59 Lebanon 76
3.60 Lesotho 76
3.61 Liberia 77
3.62 Libya 78
3.63 Liechtenstein 79
3.64 Lithuania 79
3.65 Luxembourg 80
3.66 Macedonia 81
3.67 Madagascar 82
3.68 Malawi 82
3.69 Mali 83
3.70 Malta 84
3.71 Mauritania 84
3.72 Mauritius 85
3.73 Moldova 86
3.74 Monaco 86
3.75 Montenegro 87
3.76 Morocco 88
3.77 Mozambique 89
3.78 Namibia 90
3.79 Niger 90
3.80 Nigeria 91
3.81 North Sudan 93
3.82 Norway 94
3.83 Oman 95
3.84 Pakistan 96
3.85 Palestine 98
3.86 Poland 99
3.87 Portugal 101
3.88 Qatar 102
3.89 Republic of Congo 102
3.90 Romania 103
3.91 Russia 104
3.92 Rwanda 107
3.93 San Marino 107
3.94 Sao Tome E Principe 108
3.95 Saudi Arabia 109
3.96 Senegal 110
3.97 Serbia 111
3.98 Sierra Leone 112
3.99 Slovakia 112
3.100 Slovenia 113
3.101 Somalia 114
3.102 South Africa 114
3.103 South Sudan 115
3.104 Spain 116
3.105 Swaziland 118
3.106 Sweden 119
3.107 Switzerland 120
3.108 Syrian Arab Republic 121
3.109 Tajikistan 123
3.110 Tanzania 123
3.111 The Gambia 124
3.112 The Netherlands 125
3.113 The United Arab Emirates 127
3.114 The United Kingdom 127
3.115 Togo 129
3.116 Tunisia 130
3.117 Turkey 131
3.118 Turkmenistan 134
3.119 Uganda 135
3.120 Ukraine 136
3.121 Uzbekistan 138
3.122 Western Sahara 139
3.123 Yemen 140
3.124 Zambia 140
3.125 Zimbabwe 141
4 ASIA 143
4.1 Executive Summary 143
4.2 Bangladesh 145
4.3 Bhutan 147
4.4 Brunei 147
4.5 Burma 148
4.6 Cambodia 149
4.7 China 149
4.8 Hong Kong 151
4.9 India 152
4.10 Indonesia 154
4.11 Japan 156
4.12 Laos 159
4.13 Macau 160
4.14 Malaysia 161
4.15 Maldives 162
4.16 Mongolia 162
4.17 Nepal 163
4.18 North Korea 164
4.19 Papua New Guinea 165
4.20 Philippines 166
4.21 Seychelles 170
4.22 Singapore 171
4.23 South Korea 171
4.24 Sri Lanka 173
4.25 Taiwan 173
4.26 Thailand 174
4.27 Timor - Leste, Democratic Republic of 175
4.28 Vietnam 176
5.1 Executive Summary 177
5.2 Argentina 179
5.3 Belize 182
5.4 Bolivia 183
5.5 Brazil 184
5.6 Chile 187
5.7 Colombia 188
5.8 Costa Rica 190
5.9 Ecuador 191
5.10 El Salvador 192
5.11 Guatemala 193
5.12 Guyana 194
5.13 Honduras 194
5.14 Mexico 195
5.15 Nicaragua 197
5.16 Panama 198
5.17 Paraguay 198
5.18 Peru 199
5.19 Suriname 200
5.20 The Falkland Islands 201
5.21 Uruguay 202
5.22 Venezuela 203
6.1 Executive Summary 205
6.2 Antigua and Barbuda 206
6.3 Aruba 207
6.4 Barbados 207
6.5 Bermuda 208
6.6 Canada 209
6.7 Cuba 211
6.8 Dominica 212
6.9 Dominican Republic 213
6.10 Greenland 214
6.11 Grenada 215
6.12 Haiti 215
6.13 Jamaica 216
6.14 Puerto Rico 217
6.15 St. Kitts and Nevis 218
6.16 St. Lucia 218
6.17 St. Vincent and the Grenadines 219
6.18 The Bahamas 220
6.19 The British Virgin Islands 220
6.20 The Cayman Islands 221
6.21 The U.S. Virgin Islands 222
6.22 The United States 222
6.23 Trinidad and Tobago 224
7 OCEANA 225
7.1 Executive Summary 225
7.2 American Samoa 226
7.3 Australia 227
7.4 Christmas Island 228
7.5 Cook Islands 228
7.6 Fiji 229
7.7 French Polynesia 229
7.8 Guam 230
7.9 Kiribati 231
7.10 Marshall Islands 231
7.11 Micronesia Federation 232
7.12 Nauru 232
7.13 New Caledonia 233
7.14 New Zealand 234
7.15 Niue 235
7.16 Norfolk Island 235
7.17 Palau 236
7.18 Solomon Islands 236
7.19 The Northern Mariana Island 237
7.20 Tonga 238
7.21 Tuvalu 238
7.22 Vanuatu 239
7.23 Wallis and Futuna 239
7.24 Western Samoa 240
8.1 Disclaimers & Safe Harbor 241
8.2 ICON Group International, Inc. User Agreement Provisions 242


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 2 to 5 megawatt engines 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 2 to 5 megawatt engines 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.


In order to estimate the latent demand for 2 to 5 megawatt engines 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 2 to 5 megawatt engines 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 2 to 5 megawatt engines. 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 2 to 5 megawatt engines. 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 2 to 5 megawatt engines.

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 “2 to 5 megawatt engines” 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 2 to 5 megawatt engines 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 "2 to 5 megawatt engines" as including all commonly understood products falling within this broad category, such as stationary engines used for non-utility power generation with 2 to 5 megawatts of power. Companies participating in this industry include Caterpillar, Inc., Cummins, Inc., DaimlerChrysler AG, and The Detroit Diesel Corporation. All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2015).

Step 2. Filtering and Smoothing

Based on the aggregate view of 2 to 5 megawatt engines 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 2 to 5 megawatt engines 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 2 to 5 megawatt engines 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 2 to 5 megawatt engines). 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 world's 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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Pages 242
  • The 2016-2021 World Outlook for 2 to 5 Megawatt Engines
  • ISBN: 000882876G
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